Metal forming beyond shaping: Predicting and setting product properties

G Model

CIRP-1399; No. of Pages 24 CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Contents lists available at ScienceDirect

CIRP Annals - Manufacturing Technology jou rnal homep age : ht t p: // ees .e lse vi er . com /ci r p/ def a ult . asp

Metal forming beyond shaping: Predicting and setting product properties A.E. Tekkaya (1)a,*, J.M. Allwood (1)b, P.F. Bariani (1)c, S. Bruschi (2)c, J. Cao (1)d, S. Gramlich e, P. Groche (1)f, G. Hirt (2)g, T. Ishikawa (2)h, C. Lo¨bbe a, J. Lueg-Althoff a, M. Merklein (2)i, W.Z. Misiolek j, M. Pietrzyk (1)k, R. Shivpuri (1)l, J. Yanagimoto (1)m a

Institut fu¨r Umformtechnik und Leichtbau, Technische Universita¨t Dortmund, Dortmund, Germany Department of Engineering, University of Cambridge, Cambridge, United Kingdom c Department of Industrial Engineering, University of Padova, Padova, Italy d Mechanical Engineering Department, Northwestern University, Chicago, IL, USA e Institut fu¨r Produktentwicklung und Maschinenelemente, Technische Universita¨t Darmstadt, Darmstadt, Germany f Institut fu¨r Produktionstechnik und Umformmaschinen, Technische Universita¨t Darmstadt, Darmstadt, Germany g Institut fu¨r Bildsame Formgebung, RWTH Aachen, Aachen, Germany h Metal Forming and Processing Laboratory, Nagoya University, Nagoya, Japan i Institute of Manufacturing Technology, Friedrich-Alexander-Universita¨t Erlangen-Nu¨rnberg, Erlangen, Germany j Institute for Metal Forming, Lehigh University, Bethlehem, PA, USA k Department of Applied Computer Science and Modelling, AGH University of Science and Technology, Krakow, Poland l Department of Industrial Engineering, The Ohio State University, Columbus, OH, USA m Institute of Industrial Science, The University of Tokyo, Tokyo, Japan b

A R T I C L E I N F O

A B S T R A C T

Keywords: Metal forming Product Properties

Metal forming is not only shaping the form of a product, it is also influencing its mechanical and physical properties over its entire volume. Advanced analysis methods recently enable accurate prediction of these properties and allow for setting these properties deterministically during the forming process. Effective measurement methods ensure the setting of these predicted properties. Several real examples demonstrate the impressive achievements and indicate the necessity of a paradigm change in designing products by including manufacturing-induced effects in the initial dimensioning. This paradigm change will lead to lightweight components and serve environmentally benign designs. ß 2015 CIRP.

1. Introduction Most of the energy embedded in a product is hidden in the primary energy of fabricating the material itself [7]. In this sense, metal forming is one of the most environmentally benign class of manufacturing processes due to its high material utilization [91]. Metal forming as ‘Net-Shape Manufacturing Process’ has been largely recognized as a process that shapes the final geometry of a product by plastic deformation. The fact that plastic deformation alters the mechanical properties of the product over its volume is not yet widely appreciated in design of products [24]. In the conventional cycle of product design, the designer develops the product geometry on the basis of specifications of loading conditions and material properties (material ability) of virgin materials (Fig. 1a). Due to uncertainties, a ‘safety factor’ greater than 1 is applied to the nominal load, resulting in an increase of dimensions in the final design, and embodying more energy than necessary. Generally, the manufacturing process is not considered in specifying the material ability in the design process although it can substantially alter this ability.

* Corresponding author. Phone: +49 231 7552681; fax: +49 231 7552489. E-mail address: [email protected] (A.E. Tekkaya).

Back in 2002, Zeng et al. [234] demonstrated the described importance of including information about manufacturinginduced properties in a crash simulation. The simulation result in Fig. 2a is based on nominal thickness and material properties of virgin sheet metals and showed that severe buckling occurred in the middle of the supporting beam. Fig. 2b used the tube thickness obtained after hydro-forming and manufacturing-induced material properties in the crash simulation. The simulation predicted that buckling did not occur in the middle section, instead it occurred at the corner section. This is due to the fact that over 20%

Fig. 1. (a) Conventional product design and manufacturing. (b) New approach in designing and manufacturing products,[24].

http://dx.doi.org/10.1016/j.cirp.2015.05.001 0007-8506/ß 2015 CIRP.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

2

Fig. 2. Crash simulation and test [234]: (a) simulation with nominal properties. (b) Simulation with manufacturing properties. (c) Experiment.

thinning existed at the corner during the hydro-forming process. Fig. 2c shows a photo of the real crash test, which confirmed the results of the simulation which used information about the manufacturing-induced properties. Without knowing the influence of the manufacturing process on the material properties and thickness distribution, one would arbitrarily choose a high-value ‘safety factor’ to uniformly increase the thickness across the entire tube in order to be on the safe side. In order to close the gap between the product design and the manufacturing process, the specification of the material ability and after-forming dimensions have to be incorporated in the manufacturing process, as illustrated in Fig. 1b. Given the ability of metal forming in changing mechanical properties over the entire volume of the product, a great opportunity lies in the design process if one can take a holistic approach seamlessly integrating the design of forming processes and the design of a product. This requires, on the one hand, the prediction of these properties and, on the other hand, the control of these properties in the manufacturing process. The knowledge of local material properties such as strength, formability, damage, residual stresses, texture, and microstructure can be transferred to the early stages of manufacturing and tailor-made products can be manufactured. This approach delivers a high potential to provide the sound technological basis for selecting the realistic safety factor and, hence, lightweighting the product [213]. Metal forming consists of a process chain that primarily includes thermal and mechanical processing. Table 1 summarizes the mechanical, surface, and physical product properties that can be changed by these two types of processes. Table 1 Product properties changed during forming by thermal and mechanical processes. Mechanical properties  Mechanical resistance  Impact resistance  Creep resistance  Fatigue resistance  Formability  Toughness  Residual stresses  Anisotropy  Accumulated damage

Surface properties  Surface topography  Wear resistance  Corrosion resistance  Hydrophobic resistance Physical properties  Electrical properties  Magnetic properties  Optical properties

Thermal processing consists of heat treatment processes. These processes are either applied before (annealing) or after the metal forming process (hardening or annealing). Sometimes the heat treatment is conducted together with the forming process: thermo-mechanical processing is basically used for semi-finished products (by primary forming processes), such as in rolling, extrusion, but also in part forming (secondary forming processes), such as in forging and hot stamping. Mechanical, surface, and physical product properties are finally set by cold forming processes, such as impact forging, wire drawing, roll forming, and sheet forming (Fig. 3). Machining and surface treatment processes occasionally follow the final metal forming process but basically influence only the near-surface properties. The objective of this paper is to reveal the potential of predicting and setting the mechanical and physical properties in metal formed products. Thermo-mechanical and metal forming

Fig. 3. Chain of product manufacturing by thermal, thermo-mechanical, and mechanical processing.

processes are in the focus and mere thermal processing is not covered by this paper. Several other properties, like Young’s modulus and density, that are not listed in Table 1 can be changed by plastic forming and heat treatment. However, these parameters are out of the scope of this paper. The starting point of this paper is a comprehensive knowledge about the metallurgical basis of material properties (Section 2). With the knowledge about the influencing mechanisms on the micro-scale, the prediction of macro-scale mechanical product properties during manufacturing processes becomes possible (Section 3). The achievement of the desired properties needs to be measured during the processes, requiring special monitoring techniques (Section 4). Several examples of forming processes and process chains, where the prediction and control of mechanical product properties during the forming process are already successfully performed, are reviewed (Section 5). Next, a paradigm change in the design process of products utilizing the manufacturing properties is proposed (Section 6). The paper is concluded by providing conclusions and an outlook. 2. Metallurgical basis of material properties The entire casting and deformation history has a strong influence on the mechanical and physical properties of the product achieved during forming as well as during the subsequent heat treatment operations. In general, the effects of the chemical composition of the material and mechanical process parameters on the product microstructure and final mechanical properties are very complex, but they can be simplified and conceptually illustrated as in Fig. 4. This multistep process sequence of converting a cast structure into a wrought one is often known in literature as thermomechanical treatment. This section aims at giving a basic review of the fundamental metallurgical phenomena affecting the mechanical properties of products formed under mechanical/thermomechanical processing conditions [98].

Fig. 4. Influence of the alloying elements as well as casting and forming parameters on the microstructure and properties of a steel product.

2.1. Chemical composition The chemical composition is a primary factor determining the physical properties of structural metals. On the basis of the chemical composition of the alloy and phase diagram characteristics, the

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

major strengthening mechanisms, such as solid solution, phase transformation, and/or precipitation hardening, can be predicted. Furthermore, the chemical composition controls the microstructure evolution including work hardening, dynamic recovery, dynamic recrystallization, static recovery, and static recrystallization resulting in the final grain size, rate of grain growth, formation of precipitates, rate of phase (e.g., ferrite) nucleation, rate of second phase grain growth, and so on. The composition-tailoring approach has been used in the development of different generations of sheet steels devoted to automotive applications, including three generations of Advanced High Strength Steels (AHSS). Fig. 5 indicates different automotive sheet steels developed in the last 30 years in which the chemical composition, structure, and phases were manipulated to create various combinations of ductility and strength. The area of the third generation of AHSS with yield strengths of over 2000 MPa and around 25% elongation is still under development.

3

Bauschinger effect, i.e. the lower magnitude of the yield stress after load reversal, and the absence of hardening after a load reversal are explained by the interaction of dislocations in cell-block boundaries (Fig. 6) [163]. Cell-block boundaries (CBBs) are planar arrangements of dislocations with a high relative dislocation density. According to Peeters et al. [163], dislocations with opposing Burgers vector are stopped on either side of the CBB (Fig. 6b). Upon load reversal, dislocations originally located within the core of the CBB, dislocations at the boundary and dislocations between two neighboring CBBs interact. Due to the different Burgers vector or polarity of these dislocations, annihilation takes place. This leads to the observed transients in hardening. Obviously such phenomena affect the product properties after plastic forming significantly.

Fig. 6. (a) TEM after a tensile test. (b) Schematic representation: cell-block boundaries (CBBs) and cell boundaries (CBs) [163].

Fig. 5. Combination of ductility and strength for various sheet steels for automotive applications [155].

2.3. Damage

Another example of the use of the composition-tailoring approach is given by low-alloyed steels, such as the C–Si–Mn steels, added with micro-alloying elements, such as Nb and/or V, which retard the recrystallization and grain growth since they form carbides that precipitate at the grain boundaries during forming at the elevated temperature. Therefore, this precipitation enables these micro-alloyed steels to retain higher strengths when cooled in the air after hot deformation, and, consequently, eliminates the need for quenching and tempering treatments. A similar precipitation induced hardening approach is frequently used in nonferrous metals, where the alloying elements are used to control the recrystallization kinetics or phase transformation leading to higher hardness and strength.

The prediction of failure and the knowledge of failure mechanisms are essential for successful design. Failure of a manufactured part means that it is not capable to maintain one of its functions. A classic case of mechanical failure is the occurrence of cracks in a sheet metal part. Before part failure, physical phenomena occur covering local material failure development that leads finally to part failure. As a working definition, all phenomena which contribute to the mechanical weakening of the material due to the existence or propagation of defects are summarized as damage. Common defects with such a weakening effect on different scales are: voids, cavities, micro cracks, and shear bands. Metal forming changes the damage level of workpieces and this effect can be predicted and controlled.

2.2. Work hardening

2.4. Recovery, recrystallization, and grain growth

Plastic deformation of crystalline materials is a function of active slip systems, which provide the ability for the dislocation motion [45]. During plastic deformation, dislocations propagate from the source, interact and stick to each other as well as with solutes/ precipitate particles and pile up at grain boundaries. The dislocation density, which is the amount of the length of dislocations per unit volume, increases as the plastic deformation increases, and this can be seen as the quantitative description of the plastic deformation from the metallurgical viewpoint. It lies roughly between 108 cm/ cm3 (annealed state) and >1012 cm/cm3 (cold deformed state) in steels. Resistance to the motion of dislocations increases due to the increase in interactions between dislocations and other dislocations, grain boundaries, precipitations, and other defects. The metallurgical phenomenon of work hardening is the effect of the mentioned dislocations’ interactions during forming. Different hardening mechanisms become relevant for loading involving one or more strain path changes. Inter-granular and intra-granular inhomogeneities lead to transient work hardening. Phenomena such as the Bauschinger effect [20], the retardation of hardening after loading path changes, and increases of the yield stress after certain types of loading path changes (e.g., cross hardening), are induced by the interaction of different populations of dislocation during quasi-static loading of common metals at the room temperature [89,174]. Oriented dislocation structures develop in metals during plastic deformation. The intra-granular

During forming processes carried out under hot conditions and possible soaking at different levels of elevated temperatures, metallurgical phenomena such as recovery, recrystallization, and grain growth, which are governed by diffusion, do play a role during and after deformation. Recovery reduces the energy stored in the deformed grains by a rearrangement of defects in their crystal structure and is a necessary prerequisite for recrystallization to take place. Recrystallization is the metallurgical phenomenon that allows replacing deformed grains by small dislocation free equiaxed grains at elevated temperatures. There are basically two types of recrystallization: static/metadynamic recrystallization, that occurs after plastic deformation during exposure to elevated temperature, and dynamic recrystallization, that occurs during plastic deformation at the elevated temperature. Grain growth may occur after deformation at the high temperature to further decrease the stored energy by reducing the grain boundaries extension. The effect of dynamic recovery and recrystallization on the flow stress at elevated temperatures has been studied and the role of the Stacking Fault Energy (SFE) is widely reported in literature. For the specific range of temperatures and strain rates, the balance between the two competing mechanisms of work hardening and softening can be achieved resulting in a usually constant flow stress and significantly improved ductility. Numerous studies in literature report the effect of the microstructure evolution and its influence on mechanical properties [193,208].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

4

2.5. Phase transformation and precipitation Phase transformation may take place during and after plastic deformation, both at the room and the elevated temperature. As an example, Fig. 7 shows that the microstructure of a carbon steel after hot forming may change widely on the basis of the hot forming and interpass (the state between two forming stages) parameters, resulting in different phases like ferrite, pearlite, bainite, and/or martensite depending on the applied cooling conditions [45]. Phase transformation can also be driven by straining, as it happens for some classes of steels, where the forming history dependent phenomenon of Transformation Induced Plasticity (TRIP) takes place, leading to the formation of a complex microstructure characterized by high combination of ductility and strength (see Fig. 5).

Fig. 7. Microstructural evolution of carbon steels during and after hot forming [227].

The principle of microstructure control in aluminum alloys at the elevated temperature is similar to steels in terms of control of recrystallization during interpasses. But, as aluminum is not subjected to phase transformation during cooling, the microstructure controlling strategy during cooling is quite different from that of steels. In case of aluminum alloys, solution treatment and aging are used to control the size and distribution of precipitates and intermetallic particles, which, in turn, are responsible for the final properties of aluminum alloys products [57].

Fig. 8. Grain maps generated across the extrusion weld showing the texture of the various regions [65].

of aluminum alloy extrusion welded within a porthole die [65]. The area of the weld is represented by h1 1 1i and random texture. 2.7. Thermo-mechanical processing for microstructure and mechanical property control It is evident from the metallurgical phenomena described above that different metal products with various mechanical properties can be manufactured by controlling their microstructures making use of processes combining large plastic deformation and controlled cooling. The thermo-mechanical process should be designed accordingly, in terms of deformation and cooling conditions, besides the choice of the proper chemical composition of the alloy. In this context, the material ‘microstructure’ occupies the intermediate position in the chain between the ‘process’ and the ‘mechanical properties’. For example, in case of rolling, to obtain a product with adequate mechanical properties, the sequential hot rolling process conditions must be controlled by properly selecting the hot rolling parameters, such as thickness reduction, rolling speed, rolling temperature, and interpass time, as well as the cooling conditions in terms of temperature history, cooling rate, and cooling shutdown temperature. The temperature history of such a combined process is schematically shown in the CCT diagram in Fig. 9. The temperature history of a conventional hot rolling process is represented by line (1). Graphs (2)–(4) are the temperature histories of the thermomechanical processing of plate rolling, whereas (5)–(7) are the temperature histories of ausforming processes.

2.6. Development and role of texture Crystallographic texture represents a preferred crystallographic orientation of the grains in a polycrystalline material. Examples of strong textures can be found in cold deformed wires and cold rolled sheets, which present a crystallographic orientation in the same direction as the main deformation and show a measurable degree of anisotropy. Both the presence of texture and its intensity have a significant influence on the material physical properties, leading to performances close to the ones of a single crystal in case of strong anisotropy. On the contrary, a material characterized by a random grain orientation exhibits isotropic physical properties. Texture plays a very important role in controlling quality and properties of semi-finished and finished products. For example, the use of aluminum sheets characterized by planar anisotropy close to zero allows manufacturing beverage cans with limited earing defect (Section 5.3); electric silicon steel sheets with a reduced anisotropy present reduced magnetic hysteresis useful for manufacturing of transformer cores (Section 5.6). The mechanical strength of a part after forming is also strongly influenced by the texture since the hardening behavior of the metals varies on the basis of the plastic anisotropy. The local texture, also known as micro-texture, is formed as a material response to localized straining and can be characterized in forged and extruded parts by the Electron Backscatter Diffraction (EBSD) technique, compare Section 4.2. This relatively new tool allows better understanding and control of metal forming operations, which results in controlling the physical properties of products. An example of application of the EBSD technique is shown in Fig. 8, which presents h1 0 0i texture of individual streams

Fig. 9. Temperature histories of combined thermo-mechanical processing applied to rolling [149].

3. Prediction methods Various models of different complexity and predictive capabilities are now available for determining the product properties after metal forming and in combination with heat treatment processes. Prediction of the product properties is usually fulfilled in two steps. The first step comprises the modeling of mechanical response and microstructure evolution during processing, whereas the second step tends to provide the correlation between the microstructural parameters of the material and the in-service properties of the product. The objective of this section is to provide a classification and a brief description of available models with respect to their predictive capabilities and computing costs, having in mind prospective applications.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

3.1. Classification and prospective application of models Historically, up to the 1990’s, modeling of metal forming followed a phenomenological approach, which did not distinguish between phenomena occurring at various dimensional scales and did not account directly for the material microstructure. The first developed approaches were based on analytical methods, such as the slab [27] and upper bound methods [13], which were used for the calculation of the macro-scale parameters in metal forming, namely the strain and stress fields, usually using empirical constitutive laws to describe the material mechanical response to deformation. At the same time, Johnson–Mehl–Avrami–Kolmogorow (JMAK) based models [14] were developed further to predict the microstructure evolution, and phenomenological damage models were introduced to predict the fracture occurrence, e.g., [38,80], but without being coupled with the material mechanical response. Since the early 1970’s, the Finite Element Method (FEM) has become the most popular simulation technique in metal forming [114]. Subsequently, alternative numerical methods were developed, such as the Boundary Element Method (BEM) or the Finite Volume Method (FVM), but FEM still remains the most popular approach to estimate metal flow and forces during forming. In their earlier formulations, these numerical methods made use of empirical laws (Section 3.2) to describe the material behavior and predict the macro-scale state parameters, including the temperature field. Starting from the late 1990’s, microstructural models, both phenomenological and physically-based, have been implemented into the FE codes making it possible to carry out fully coupled thermal-mechanical-microstructural simulations [168], giving rise to new challenges in modeling materials processing since they allowed to predict the evolution of the microstructural constituents (Section 3.3). At the beginning of the 21st century, the prediction of phenomena accounting explicitly for the granular structure of polycrystals was the main challenge, which led to the development of polycrystalline modeling of material behavior allowing fully multi-scale predictive models (Section 3.4). In multi-scale modeling, FE codes are still used to predict the macro-scale parameters, but they are coupled with discrete methods, such as the phase-field approach, Cellular Automata (CA), Monte Carlo (MC), or Molecular Dynamics (MD), which account for the behavior of each microstructural constituent at the micro-scale level [6,130]. The symbiotic relationship between modeling granularity or fidelity and the computational efficiency is shown pictorially in Fig. 10, where efficient applications of these models are shown at the bottom of the plot. As expected, the increase of the models’ predictive capabilities results in increasing computing costs.

5

determine microstructural changes, and derive relationships between microstructure and the resulting product properties using regression analysis. A widely used empirical relationship is the HallPetch equation that relates the yield stress (initial flow stress) and ultimate tensile strength (UTS) inversely to the square root of the grain size [1]. Consequently, the yield stress increases as the grain size is refined. However, it has been noted that, for very small grain sizes (in the nanometer range), yield stress falls with reducing grain size. This is known as the ‘inverse Hall-Petch relationship’. For low carbon steels (C < 0.2%) produced as flat hot rolled products, Pickering [167] proposed well-known empirical equations that relate chemical compositions and grain diameters to the yield and ultimate strengths, and elongations in the rolling and transverse directions. He provided the relations for low carbon low-alloy mild steels, low carbon ferritic-pearlitic steels including high-strength low-alloy steel (HSLA), and medium-high carbon ferrite-pearlite steels. Using a similar empirical approach, Gladman [66] derived the relations between composition, thermal processing, and mechanical properties for air hardening microalloyed steels produced as hot rolled bars or sheets. These relations are also applicable to medium carbon (0.35 < C < 0.50%) vanadium, niobium, and/or titanium modified micro-alloy steels used in forgings. Due to structural transformations during rapid cooling and tempering, these relations are not available for Quenched and Tempered (Q&T) steels. Fig. 11 shows a schematic of the computer assisted transition from microstructure and chemical composition to stress-strain curves. In this investigation, 15 kinds of steel sheets with different chemical compositions and phases like ferrite, pearlite, martensite, and bainite are tested, and they are integrated in the prediction system for stress-strain curve at cold state by using the mixture rules. The coefficients of the Swift equation for flow stress can be predicted by the state of the microstructure (grain size, phases, etc.) and the empirical equations for various steel grades. Similar empirical equations were obtained for monolithic bainitic and martensitic structures. The kinetic properties of a ferrite-pearlite structure can be estimated by the mixture rules, but they cannot reflect the morphologies of steels.

Fig. 11. Computer-assisted transition from microstructure and chemical composition to stress-strain curve [217].

3.3. Mechanical and microstructural predictions during processing

Fig. 10. Classification of models in metal forming with respect to their predictive capabilities and computing costs.

3.2. Empirical models The simplest approach to predict mechanical properties of formed products is to carry out controlled forming experiments,

Constitutive laws implemented in the models of metal forming processes are used to relate the process parameters to the material flow stress [169], to the evolution of microstructural features and phenomena, such as the grain size [160], phase transformations [48,117], precipitation kinetics [50], and to the damage evolution [231], which, in turn, provide the basis for the prediction of the mechanical properties in the product, such as yield strength and ductility, fatigue and fracture resistance. When processing under cold conditions, the modeling of material texture assumes importance, especially in case of sheet forming processes [34]. The constitutive laws describing the material flow behavior and evolution of microstructural characteristics, including damage, can be broadly classified into statistical, phenomenological, and mechanistic models [63]. Statistical models: Statistical models require a large amount of experimental data to derive a mathematical relationship through regression fitting between the independently varied process

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 6

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

parameters and the resulting microstructural features. For multivariable analysis, well known statistical techniques, such as the Design of Experiments (DOE), Response Surface Method (RSM), and analysis of variance (ANOVA), are used for selecting the most suitable process parameters (e.g., heat treatment parameters [125]). The statistical models are effective for process optimization at reasonable computational costs, however, they present several drawbacks, e.g., the experimental data may mask second order or coupling effects making it difficult to capture fundamental physics, the lack of prior knowledge on the dominant metallurgical phenomena makes the selection of a suitable fitting function rather difficult, and the extrapolation outside the range of the process parameters used for their validation may introduce considerable prediction errors. The first two drawbacks may be mitigated using Artificial Neural Network (ANN), based on multivariable non-linear regression models. Recently, phenomenabased ANN models have been developed to predict material transformations with higher accuracy [23]. Phenomenological models: The phenomenological models define the relationships between the process variables and the microstructural characteristics, making use of equations describing microstructural phenomena, such as recovery, recrystallization, grain growth, and precipitation, even if not directly derived from fundamental theory. These models are implemented into the process models to predict the evolution of the microstructural state and, possibly, the mechanical properties in the product, both off-line and in-line, e.g., in hot rolling of rods and strips [160,166]. They have two main drawbacks: first, their material parameters have to be determined empirically, and second the form of the regression fit may not capture the possible changes in the underlying microstructural phenomena. Most of the phenomenological microstructural models are based on the JMAK theory [14] or further modifications for a more accurate description of recrystallization, phase transformation, and precipitation kinetics, with particular emphasis on strain induced precipitation [51]. In case of most non-ferrous alloys, the description of precipitation hardening that may take place during the deformation process itself or the heat treatment becomes mandatory for the prediction of the product properties. Phenomenological models used to describe damage evolution and fracture when a damage variable reaches a critical value are based on fracture mechanics [134], which is usually derived from energy calculation assuming that the deforming material is porous-free [43,159]. Although the damage variable is history dependent, it is not coupled to deformation and it does not modify the yield function. However, the easy calibration and implementation of fracture mechanics models into FE codes favors their wide utilization. The basic challenge in fracture mechanics is that displacements are discontinuous through the crack, and more accurate modeling needs special techniques such as remeshing, meshless or extended FE method (xFEM) to model this discontinuity. Mechanistic models provide the fundamental understanding of the physics governing the phenomena involved in the process to be modeled. Mechanistic models have the ability to extrapolate beyond their calibration range as long as the controlling phenomenon remains unchanged. Their major drawback lies in a large amount of computational resources usually needed to solve even the simplest problems. They can present different levels of complexity, from the mentioned Hall-Petch relationship for the yield strength prediction, to strain and strain rate hardening models based on dislocation mechanics, crystal plasticity models based on texture evolution [175], and the more recent phase field approach. Internal Variable Methods (IVM) are models based on dislocation mechanics: they combine the material flow stress with the microstructure, being the dislocation density as the measure of the material deformation. In IVM, the material response is a function of external variables, internal variables, and time, therefore accounting for the process history. Single internal variable models, accounting for the average

dislocation density, are based on the fundamental works of Mecking and Kocks [139], Estrin and Mecking [58], and Sandstro¨m and Lagneborg [184], but approaches based on two state variables, three state variables, and distribution function for dislocation density have been recently introduced [176,178]. The capability to describe the material behavior and microstructural evolution during the interpass time between subsequent deformation steps and prediction of product properties combined with reasonably low computing costs are the main advantages of the IVM models (Fig. 12).

Fig. 12. Mechanistic models.

In recent years, the phase field approach [148] has emerged as one of the most powerful methods for modeling many types of microstructure-evolution processes, including the austenite decomposition. The phase-field model treats a polycrystalline system, containing both bulk and boundary regions, in an integral manner. Mechanistic models to predict fracture are void-growth based criteria and Continuum Damage Mechanics (CDM) based criteria; the former predicts the evolution of voids in porous ductile materials and uses analytical formulae describing isolated unit cells with voids under remote stress and strain fields. Since local stresses at micro-scale may differ from the stresses at macro level, a partial insight into the underlying micro mechanisms is needed for fracture modeling, see Gurson’s model [80] and its improvement by Tvergaard–Needleman [153]. On the other hand, in the framework of CDM, according to [104], the amount of damage can be characterized by the area fraction of voids at the considered cross section. Chaboche proposed a similar approach [38], using Young’s modulus in the definition of damage. Models based on CDM modify both the yield function and the elastic behavior as the deformation and damage progress, thus providing a full coupling with the material constitutive behavior [124,231]. 3.4. Multi-scale, multi-physics, and multi-resolution models Multi-physics and multi-resolution models are located at the right top corner in Fig. 10. These models have wide predictive capabilities and are considered the most novel solutions in the field of metal forming. On the other hand, they require very long computing times, which may reduce their practical application. The idea of the multi-scale approach is applied now in all novel solutions involving multi-physics and multi-resolution problems. Multi-scale modeling is carried out for the purpose of capturing fundamental aspects of the material behavior under heterogeneous deformation. Nano- and micro-scale models based on discrete methods are introduced to describe mechanisms such as grain boundary sliding and rotation, interactions between dislocations and grain boundaries, micro-cracks initiation and propagation, and many more. As a consequence, several inaccessible deformation modes can be simulated by the unified constitutive equations. The general idea of the multi-scale modeling is presented briefly below, whereas more details can be found in [31,130].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Fig. 13. Multi-scale modeling: upscaling models and concurrent multi-scale computing.

The methods are usually classified into two groups: upscaling methods and concurrent multi-scale computing (Fig. 13). In the upscaling class of methods, constitutive models at higher scales are constructed from observations at lower, more elementary scales by employing the concept of Representative Volume Element (RVE). The computational homogenization methods [147] are considered to belong to this group. Simulation of dynamic recrystallization during hot rolling using FE in the macro scale and CA method in the micro scale [64], so-called CAFE method, is a typical example of the upscaling approach. In concurrent multi-scale computing, the problem is solved simultaneously at several scales by an a priori decomposition of the computational domain. Two-scale methods, whereby the decomposition is made into coarse and fine scales, have been considered so far. The method used in the fine scale is applied to a part of the whole domain of the solution. It can be either the same method, which is used in the coarse scale, for example, the FE method, or it can be a discrete method (CA, MC). In the former case, the extended finite element (xFEM) and the multi-scale extended finite element (MS-xFEM) methods can be distinguished. An approach presented in [93] for incremental bulk metal forming is an example of the concurrent computing. Cellular Automata (CA) is one of the commonly used methods for modeling micro scale phenomena. The CA lattice of cells represents a two- or three-dimensional microstructure and reproduces topological relations between grains. These relations include the length of the grain boundaries as well as selected properties, e.g., misorientations. The state of each CA cell is described by the state variables, each cell is described by its state and the values of the internal (state) variables. The state of the cell at each time step is controlled by the transition rules [225]. The CA method was widely used in various fields of research, and in metal forming, it was successively applied to simulate strain localization [132], dynamic recrystallization [84], cold deformation and annealing [129], and other phenomena. Monte Carlo (MC) is a general name for a group of algorithms based on a random sampling of a solutions space V for application in mathematical and physical simulations [62]. In the case of microstructure evolution models, the algorithm usually follows an iterative procedure over a computational domain composed of lattice sites in a specific defined orientation (state). In the first step, a random lattice site is selected and the energy for that site is computed. In the second step, the selected lattice site is reoriented to the orientation chosen randomly from possible states. The new site orientation is accepted when the new energy is not higher than the previous one. With successive MC steps, the energy value of the entire system is reduced, and, e.g., the digital microstructure with clearly visible grains can be observed. The MC method is very similar to the CA method and has analogous advantages when modeling of material properties is considered. However, the MC method uses arbitrary units and the basic rule for this method is energy minimization. On the contrary, the CA technique uses physical units and it cooperates with analytical equations, thus it provides greater possibility for practical applications.

7

The Digital Material Representation (DMR) approach allows a description of real material morphology with different micro scale features directly included (e.g., precipitates, inclusions, big and small grains, grain boundaries, crystallographic grains orientations, phases boundaries). DMR can be defined as a material description, based on measurable quantities, which provides the link between the simulation and the experiment (Fig. 14) [194]. The DMR allows a virtual analysis of real material behavior, while errors of calculations are minimized [207]. Numerical models based on the DMR give more detailed predictions than those based on conventional closed form or differential equations based approaches because they take into account complex microstructure morphologies in an explicit manner during simulation [131]. The validation of simulation results obtained by DMR can be done using Digital Image Correlation (DIC) combined with an in-situ tensile test inside a scanning electron microscope, which allows the observation of microstructure deformation during loading [128]. Molecular Dynamics (MD) is another simulation method, which is extensively applied at the atomistic level. Applications are found for simulating plastic damage [127] or the interface and lubricant behavior [229].

Fig. 14. General idea of the Digital Material Representation.

Multi-resolution models belong to the concurrent computing group, as well. Recent work has successfully integrated material design with the shear instability or final fatigue/fracture properties [54,137], and subsequent stress collapse. The former is responsible for dynamic adiabatic shear band propagation and are captured by including the effects of shear driven micro void damage in a single constitutive model [138]. The multi-resolution approach improves not only the speed but also the accuracy, to which the final behavior of complex materials and material systems designs through the forming processes can be predicted. The physics is captured at different resolutions, for example, void mechanics, dislocation mechanics, dislocation density, fracture prediction, damage accumulation, material localization, and fatigue life, see Fig. 15.

Fig. 15. Illustration of multi-scale domains in the physical model and in the computational domain [220].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 8

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Embedded constitutive models implicitly predict scale separation through homogenization such that scale specific physics can be retained without modeling explicitly, history variables such as volume fraction can be tracked at their respective levels, the impact of mesh dependence and traditional scale separation issues are reduced, and inhomogeneous deformation can be analyzed naturally. The theory was used to predict the fracture behavior and improve the fracture toughness of steel alloys designed by experimentally reconstructing the microstructure of the materials to yield a hybrid multi-scale mechanics-experimental analysis, as shown in Fig. 16 [215].

Fig. 16. Multi-scale simulation of three-dimensional ductile fracture process. (a) Experimental specimen. (b) Microstructure reconstruction at crack tip. (c) Simulation results of process zone. (d) Simulation results of microstructure deformation and evolution inside process zone. [215].

3.5. Deterministic and probabilistic predictions Multi-physics modeling allows capturing different physical phenomena that may arise during the technological process. Typical processes that require multi-physics simulation tools are welding, solid structure interactions, or solidification. As far as metal forming is concerned, thermal-mechanical-microstructural coupling has been used for decades to capture various phenomena occurring during deformation [169]: Fig. 17 represents this coupling, which employs FE models to describe the mechanical and thermal phenomena, while a variety of methods described above is used to account for the microstructural phenomena. As new forming processes are emerging that exploit energy means different from the mechanical one (e.g., electro-magnetic forming [171] or electroplasticity-based processes [44]), the effects of the electrical or magnetic fields on the material behavior, both in terms of mechanical response to deformation and microstructural evolution, can be captured only if fully coupled multi-physics models are developed.

Process uncertainty, variance, and risk: Experimental evaluation of product properties and performance shows considerable variance or scatter in the results. Knowledge discovery models [3] have been used in investigating the role of material and process uncertainty in scatter. A simple approach is to introduce uncertainty in a deterministic process model through variance in the material and process properties and constants. Alternatively, the material and process constants can be taken as statistical distributions: often assumed to be normally distributed with two independent parameters (mean and variance). Using established methodology of statistics, material (mechanical properties and microstructure) and process (friction, die velocities, blank holder pressure, etc.) variance can be converted to variance in the product properties and tolerance. Material and process uncertainties in forming processes can also have considerable influence on the fatigue and fracture performance of safety-critical parts. The failure of these parts can lead to the failure of the entire system, increasing societal risk. In developing models for such applications, forming models have to be linked to the probabilistic models of system performance. An example of such a probabilistic model is the hierarchical Bayesian model developed for estimating failure risk in hot forging of a titanium aeroengine disc with hard alpha inclusions, see Section 5.5. An alternative approach is to explicitly model the cause of failure, such as inclusion or voids, to model their evolution of voids during processing, and to model their effect on product failure. Many higher level methods have been developed for modeling defects such as level set methods and multi-body methods. Examples of probabilistic design and process reliability in sheet metal forming control include those of Zhang et al. [235]. 4. Property monitoring A large number of measurement principles is available for the determination of mechanical properties. Table 2 summarizes

Table 2 Available measurement methods for mechanical product properties and their capability of measurement. Property Surface morphology

Microstructure

Yield and ultimate strength

Fig. 17. The idea of the multi-physics modeling of metal forming, s—stress, e— strain, T—temperature, Q—heat generated due to transformations, X—volume fractions of phases.

Process optimization: While most of the effort in process or die optimization in metal forming uses FE based approaches, these efforts are often ‘‘trial’’ and ‘‘error’’ type. More formal use of the design methodology and artificial intelligence (AI) in the optimal design of a die or a forming process include sensitivity analysis (e.g. in the design of hot rolling passes [102]), genetic algorithmic search (e.g. optimal design of drawing dies [179] and multi-stage processes [180]), knowledge based design, ANN, fuzzy reasoning [200] and reverse engineering. Metamodeling is commonly used in optimization, as well [116].

Defects (cracks) and damage voids)

Residual stresses

Measurement principle White-light interference microscopy [40,103] Tactile profilometer [74,136] Photometric technique [157] Laser triangulation [198] Electronic speckle pattern interferometry [94,165] Near field scanning optical microscopy [94,191] Atomic force microscope [41,86] Electron backscatter diffraction [81] Thermal etching [22] Scanning electron microscope + focused ion beam [87] Tensile test [95,221] Compression test [34,111] Hardness measuring [214] Creep rupture test [135] Shear test [32] Plane torsion test [170,230] Impact test [39,204] X-ray diffraction [77,99] Magneto-inductive testing [42] Thermography [75,79] Ultrasonic [21,28] Radiographic inspections [67] Vibrometry [110,226] Acoustic emission [108] Eddy current techniques [233] Magnetic leakage-flux [210,216] Observation of micro-sections [126] Density measurement [209] X-ray diffraction [16,185] Hole drilling [16] Neutron diffraction [16] Focused acoustic waves [185]

Application

Type

In-situ

Offline

X

X

X

X X X X

X X X X

X X X X

X

X

X

X

X X X X X X X X X

X

X X X X X X X X X X X X X X X X X X X X X X X X X X

DT

X X

NDT

X X X

X X X X X X X X

X

X X X X X X X X X X

X X X

X X X X X

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

applicable measurement principles and categorizes them according their capability into in-situ or offline as well as destructive (DT) or nondestructive (NDT) specimen testing. A measurement principle is categorized as in-situ if process control is feasible without specimen sampling and if the evaluation time is shorter than the time during which process parameters change significantly. For an offline measurement, the specimen is sampled and investigated outside the production line. Hence, the evaluation time can be longer than the time during which process parameters change [109]. In the following sections, selected new methods of measurement that are significant for the determination of mechanical properties in metal formed products will be discussed.

9

Fig. 19. Schematic illustration showing the combined configuration of the SEM and FIB (left) and an example of observation [87].

4.1. Measurement principles of surface morphology

4.3. Measurement principles for yield strength

Surface morphology is usually determined by mechanical or optical means. An often-used principle for surface observation, with a topography resolution between 0.1 nm and 100 mm [74], is white-light interference microscopy (WLIM). By using chromatic white light (CWLIM), an inline measurement is possible due to the omission of the measurement orthogonal to the surface. CWLIM is a non-destructive method and is based on the evaluation of different wavelength intensities depending on the surface topography (Fig. 18).

Yield strength measuring procedures are required to characterize new materials or to ensure that properties correlate with the ones assumed during product dimensioning, respectively. Due to the fact that the measuring procedure is heavily dependent on the field of application, many different measuring procedures are available. The procedures can be grouped by the type of load (tension, compression, bending, shearing, and torsion) and the stress time (quick, steady, abruptly and oscillating). Most of the procedures are destructive and cannot be used for in-situ measurements (see Table 2). An approach to measure the yield strength of cold formed products locally is using hardness measurements [214]. Fig. 20 shows the determination of the strength of a cold extruded rod. The hardness values are converted to the strength by

Fig. 18. Schematic of a chromatic-confocal displacement sensor [103].

Another non-destructive principle for surface roughness is laser triangulation. This technique bears on the measurement of elapsed time of laser light or the detection of different light intensities due to reflection [198]. As a result of using laser light in combination with fast data processing, this method is useable for an in-situ measurement. The same applies to the photometric technique [157]. In contrast, near field scanning optical microscopy, with a lateral resolution better than 80 nm [191] is, based on its elaborated test setup, only useable offline [112]. 4.2. Measurement procedures for microstructures Measurement procedures for microstructures, like the electron backscatter diffraction (EBSD), are essential for understanding material behavior during a forming process and the resulting properties. EBSD is founded on the physical principle that accelerated electrons get characteristically diffracted when striking crystallographic planes. EBSD has become a well-established technique in order to obtain crystallographic information, such as structure and orientation, from samples. A new method for analyzing microstructure evolution of aluminum during deformation (dynamic recrystallization) is presented by Gu¨zel [81]. In this case, the determination of microstructure serves to estimate the hardness of final aluminum profiles (see Section 5.1). EBSD itself is a nondestructive procedure. However, for analyzing grain structure evolution, a section needs to be prepared, whereby the procedure could not be classified as non-destructive. In Fig. 19, the combined configuration of a scanning electron microscope (SEM) and a focused ion beam (FIB) for microstructure analysis is given. This arrangement has the benefit that high resolution and high contrast can be obtained.

Fig. 20. Measurement of the local strength of a cold extruded product by Vickers hardness measurements [214].

s f ðe ¼ 0:112Þ ¼

9:81  HV 2:475

(1)

where HV is the Vickers hardness number and s f is the flow stress at an offset of strain e = 0.112. This method is offline and nondestructive if the strength is measured on the surface of the product and destructive if measured in the interior. A recently developed online method based on X-ray diffraction [77,99] allows the measurement of surface stresses during plastic deformation. The application of this novel technology as presented in [78] to a bending process is shown in Fig. 21. Magneto-inductive testing is a NDT procedure for the in-situ measurement of hardness or UTS. The method is based on the correlation of electro-magnetic eddy current parameters and mechanical material characteristics. By the so-called harmonics

Fig. 21. X-ray diffractometer mounted on the bending machine [78].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 10

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

analysis, parasitic couplings like temperature can be eliminated. An example of application is the measurement of the hardness of press-hardened B-pillow parts [42].

both cases, the measurements provide the data for the calibration of models predicting the material formability. 4.5. Measurement of residual stresses

4.4. Measurement principles for defect and damage detection Several non-destructive techniques are available to detect defects (cracks) and damage [209] inside a component after the production process. Vibrometry and acoustic emission are based on the observation that a defect part has a different acoustic fingerprint than an undamaged part [108,110]. Both procedures are basically capable for inline quality assurance. Certainly, due to external vibrations, examination may have to be conducted outside the production line (offline). Another common procedure to detect defects is the use of micro-sections. Owing to the fact that the generation is time-consuming and it is a DT procedure, it only can be used offline. In contrast to that, thermography is a non-destructive and noncontact method to analyze subsurface defects in almost all materials by detecting emitted infrared radiation. For detecting defects, the component needs to be thermally activated. In the case of active thermography, the thermal load is applied by an external heat source [75], in the case of passive thermography, the heat is generated due to the conversion of mechanical energy (e.g., ultrasonic waves) into thermal energy [79]. Due to the fact that different materials have various thermal coefficients, which lead to different heat fluxes and subsequently to different surface temperatures, defects are detectable. In Fig. 22, a schematic test setup for an active thermography observation is given.

Fig. 22. Test setup for an active thermography observation [9].

Like thermography, ultrasonic testing is a non-destructive technique. The procedure is based on the interpretation of reflected ultrasonic waves. For undamaged regions, it is typical that there is only a front surface and back wall echo. In case of an existing damage, an additional wave is reflected or the back wall echo is reflected earlier. By analyzing several process parameters also the depth of damage can be deduced. To launch the waves into a specimen, a coupling medium is almost always required [28]. The coupling medium can be placed on the surface or the whole specimen is placed in a liquid [21]. Since it is not always possible to use a coupling medium, air-coupled varieties have also been developed [28]. Thereby, the application of ultrasonic testing is capable for inline measuring [46,88]. Computed-tomography (CT) is a radiographic inspection principle. Like all radiographic testing, CT is a NDT technique. This technique relies on the measurement of the intensity of Xrays, which is affected by the density distribution in a defective specimen (e.g., flaws or cavities). In contrast to conventional X-ray studies (two-dimensional images) CT compiles a volumetric image of the specimen [67]. Beside fault detection, CT is also useable for three-dimensional geometrical form surveying [152]. By using micro experimental setups, in-situ measurements are feasible [120]. In the field of metal forming, the main area of application of the micro-CT is the characterization of damage evolution and ductile fracture in terms of identification of nucleation, growth and coalescence of micro-voids. This technique can be applied either during the deformation itself in order to track the progression of damage as it evolves, providing in-situ measurements [120], or after fixed levels of deformation in order to evaluate the accumulated damage, providing ex-situ measurements [107]. In

Due to the fact that a considerable part of the total stresses during operation are residual stresses, their observation is important. Two techniques are mostly used. One is the hole-drilling. The principle is, due to small hole-diameters, a semi-destructive technique [101]. By drilling a hole in the surface, existing stresses will relax. This relaxation can be detected using, e.g., strain gauges. As the deformations are small, this technique requires expertise. On the other hand, X-ray diffraction (XRD) is an NDT which is applicable in an industrial environment. To determine residual stresses by XRD, an X-ray beam is adjusted to the surface. Due to diffraction on crystallographic planes, the beam is characteristically reflected as a function of the Bragg angle. Differently orientated planes cause different diffraction patterns on a detector. By comparing the pattern of an unloaded specimen with that of one with residual stresses, the value of the residual stresses can be determined. Likewise, an NDT method is the measurement of residual stresses by ultrasonic waves (UW) [101]. This method is based on the fact that the travel time of UW is dependent on the stress. By knowing the travel time of UW in an unstressed specimen, changes in the stress level can be determined.

5. Application to forming processes Products are manufactured in process chains. The final product properties are influenced at the beginning by producing the semifinished products such as sheets, wires, and profiles. Subsequent changes of properties occur in component forming processes such as forging, stamping, and bending. Dependent on the temperature, the forming processes are divided into thermo-mechanical and mechanical processes, in which different principles govern the final properties. The following examples cover the controlling and prediction of hardness, formability, anisotropy, residual stresses, damage, and electromagnetic properties (Fig. 23). Besides rolling, extrusion and severe plastic deformation processes to manufacture semifinished goods, also the possibilities of forging, stamping, and bending techniques are summarized to enhance the product quality through defined product characteristics.

Fig. 23. Overview of discussed properties and processes.

5.1. Product property: Hardness Beyond the shaping of products, several forming processes are designed to obtain a predefined hardness distribution. The following examples depict the setting of hardness through thermo-mechanical processing. In this context, the processes such as hot forging, thermo-mechanical rolling, hot stamping, and hot

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

11

extrusion present the effect of controlled grain size and volume fractions on mechanical strength properties. Hardness control in hot forging: The prediction of hardness and control of microstructure of steel alloys during hot forging processes has been in the focus of research for decades. Wang et al. [224] deliver empirical formulae (see Section 3.1) for the microstructural effects during multi-stage hot forging of microalloyed steel and set them into correlation with the resulting workpiece hardness. The formulae are incorporated into the thermo-coupled rigid-plastic FEM code DEFORM-2D. Starting from an expression for the austenite grain size as a function of forging temperature and holding time, the volume fraction of ferrite grain Vf is obtained: V f ¼ 13:6 þ 0:5 mm=Dg  5:5 s=K  u

(2)

here Dg is the austenite grain size in mm and u the cooling rate. The relation between Vickers hardness HV and volume fraction of ferrite grain is expressed by: HV ¼ 281  103  V f

(3)

The complete forging process can be modeled by combining the FEA with the analytical grain size estimation model for each time step. Fig. 24 shows the comparison of simulated and experimental hardness distribution in a workpiece obtained by a four-stage forging process.

Fig. 24. Hardness (HV) in forged part. Original billet size: Ø 46  64.8 mm [224].

Yukawa et al. [232] present a so-called Virtual Laboratory System (VLS) for the FEM-based calculation of recrystallization and precipitation behavior as well as the transformation behavior of steel during forging processes. The strength distribution of the final product is predicted. The VLS consists of different modules covering different aspects of a hot forging process of medium carbon steel adding the micro alloying element vanadium. Fig. 25 shows the different VLS modules, which are based on different thermodynamical and mechanical equations (see Section 3.3).

Fig. 25. Image chart of VLS modules [232].

The VLS is used for the analysis of a forging process aiming at manufacturing of graded products that combine high strength and good machinability. Fig. 26 shows exemplary temperature-timecourses for the fabrication of graded workpieces.

Fig. 26. Examples of forgings with graded strength and controlled forging process to produce them [232].

Microstructure control in thermo-mechanical rolling: A process specifically designed to control the product – especially microstructural – properties, is called thermo-mechanical (TM) rolling. TM rolling is not only used to obtain the desired final shape of the product but is also an important tool to influence the final strength. Important properties that are tailored by the rolling process are microstructure in terms of grain size and phase composition. Conventionally, reheating and quenching are employed used after hot rolling to obtain the final product properties. TM rolling aims at achieving the final product properties during hot rolling, circumventing further processing. The microstructure is very sensitive to the process parameters, and thus understanding and controlling the influence of the process parameters, hence, is crucial for obtaining the desired product properties [158]. A typical TM rolling setup consists of a reheating furnace, a reversing roughing mill, a finishing mill, a runout table for laminar cooling, and, finally, a down coiler. To illustrate the enormous possibilities for varying the process parameters (rolling schedule, cooling temperatures and rates, etc.) the process chain to manufacture a crash box starting with the finishing mill is shown in Fig. 28. The mechanical property control is mainly achieved in the finishing mill and by laminar cooling afterwards. The influence of both will now briefly be discussed on the basis of two case studies. The abilities of subsequent combined cold rolling and continuous annealing processes to generate final properties will be shown afterwards. The effect of thermo-mechanical hot rolling compared to conventional rolling was investigated by Shahtout et al. [197] for ten different rolling schedules with regard to the final microstructure of high strength, low carbon micro-alloyed steel (see Table 3). The effect of the laminar cooling was studied by Sha et al. [196] using a low-carbon strip steel. The authors kept all process parameters constant while varying the coiling temperature. The finishing mill delivery temperature was around 870 8C and the time for laminar cooling was 17.7 s yielding different cooling rates for all three cases. Increasing cooling rates lead to decreased ferrite volume fractions owing to a much faster phase transformation from austenite to ferrite (Table 4 and Fig. 27). The prediction by the used fast semiempirical model is in good agreement with the measurements, too. The number of stands has significant influence on the microstructure. Using less stands decreases the grain size in all cases due to increased strains and, therefore, promotes recrystallization in every pass. In the later passes, precipitates of microalloying elements hinder grain growth, resulting in a fine-grained microstructure, as clearly visible in Fig. 29. According to the HallPetch relation, the corresponding strength alters for the four-stand schedules from 603 MPa (750 8C) to 632 MPa (800 8C) and up to 618 MPa (850 8C) [197]. The semi-empirical models (see Section 3.1) used by Sha et al. accurately predict properties like the volume fraction of phases or the average grain sizes. On this basis, furthermore, an estimation of

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

12

mechanical properties, like tensile strength or elongation, is achieved [196]. Table 3 Measured ferrite grain size after thermo-mechanical hot rolling for different stand numbers and finishing temperatures [197]. Measured ferrite grain size in mm for different finishing temperatures

Number of stands

6 5 4

750 8C

800 8C

850 8C

18.9 14.5 8.25

11.4 9.1 6.43

12.1 8.6 7.13

Table 4 Cooling rate (CR), cooling temperature (CT), and measured volume fraction for ferrite and pearlite as presented in [196].

(a) (b) (c)

CR in K/s

CT in 8C

10.5 13.5 18.7

680 625 540

Ferrite vol. frac. in %

Pearlite vol. frac. in %

Meas

Sim

Meas

Sim

87.3 84.7 82.9

85.5 84.6 82.5

12.7 15.3 17.1

14.5 15.4 17.5

Fig. 27. Microstructure of the final product for different cooling rates before the finishing mill [196].

Microstructure control during finishing operations: Beyond hot rolling, important product properties are also tailored during the combined cold rolling-continuous annealing sequence. This sequence allows controlling phase composition and influencing product quality. AHSS are a spectacular example of improvement of properties by control of phase transformations [97]. These steels are subject to complex thermal cycles which give a combination of the soft ferrite and hard constituents (bainite, martensite, retained austenite). There are still possibilities of improvement of these steels by control of morphology and composition of hard constituents. Modeling of manufacturing and exploitation of an automotive part (crash box) was selected to demonstrate capabilities of multi-scale modeling in prediction of product properties (Fig. 28). Parameters transferred between operations are: austenite grain size, flow stress, strain, ferrite and martensite volume fractions. Control of properties in the hot part is described previously and modeling of this process can be found in [118]. The results of modeling of final operations of cold rolling, continuous annealing, and stamping are presented below together with the results of simulations of crash test. FE was used for macro scale and CA for micro scale analysis, see Section 3.4. Modeling started with generation of the ferritic-pearlitic microstructure after hot rolling. Here, the idea of the representative volume element (RVE) was used (1 in Fig. 30). This element was deformed in cold rolling. FE mesh reproduced grain structure and local accumulation of the energy of deformation was calculated. These data were transferred to the CA model, which simulated ferrite recrystallization (2 in Fig. 30) [129] followed by phase transformation (3 in Fig. 30) [84] during heating in the continuous

Fig. 29. Micrographs for (a) conventional hot rolling with 6 stands and TM controlled hot rolling with 4 stands for (b) 850 8C, (c) 800 8C and (d) 750 8C finishing temperature [197].

annealing. Ferrite-austenite microstructure was calculated, including non-uniform carbon distribution in the austenite in the intercritical temperature. These data were the input for the CA model for the phase transformation during cooling (4 in Fig. 30). The model predicted ferritic-martensitic microstructure as well as carbon distribution in the martensite. Local carbon concentration in the martensite allowed calculating the distribution of the flow stress in this phase. The RVE with the microstructure after continuous annealing was attached to the FE code, which simulated the stamping process and crash tests of the final product (Fig. 31). The relation between microstructure (volume fraction and morphology of phases and properties of martensite) and capability of steel to accommodate energy of deformation was evaluated. Accounting for the local hardening during stamping and crash test improved the agreement of predictions with experimental data for energy accumulation. Tailored hardening in hot stamping: Hot stamping is an established manufacturing process to adjust the hardness of final products [105]. Direct and indirect hot stamping are the two main process variants to manufacture high-hardness parts such as safety-relevant components like the B-pillar (Fig. 32). The direct hot stamping process consists of three steps. Firstly, an aluminum-silicon-coated blank of boron-manganese steel 22MnB5 is austenitized in a roller hearth furnace by conduction or by induction [115]. Afterwards, the heated sheet is transferred to the forming press, in which it is formed and quenched simultaneously. If the cooling rate of the boron-manganese steel is higher than 27 K/s, a martensitic microstructure [142] with a hardness of 470 HV (1500 MPa) develops out of the soft raw material (170 HV, 600 MPa) [19]. Since the forming operation takes place at the elevated temperatures, also forming limits are increased, while springback is reduced, additionally. Finally, the component is cut to its final geometry. Within the indirect process, the component is formed in cold condition to 90 % of its end shape first. Afterward, the conventional hot stamping is performed, in which the forming of the last 10% to the final geometry takes place [154]. Because of the poor ductility of 5% to 6% of hot stamped components, extensive efforts are made to produce components with locally adjusted hardness complying with the requirements of a good crash-performance regarding structural integrity and energy absorption [146]. One solution is to manufacture tailored welded blanks or intrinsic tailored blanks.

Fig. 28. Typical process steps for thermo-mechanical hot rolling followed by cold rolling, continuous annealing and manufacturing of final product.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Fig. 30. Multi-scale modeling of the manufacturing cycle of AHSS products for automotive industry [84,129].

13

profile, leading to the development of different microstructures within the sheet. The change in the time-temperature profile can be done before, during, or after the hot stamping process [181]. Partial austenitization strategies of the blank take place before the hot stamping process. Thereby, the sheet is heated above Ac3 temperature in certain regions where martensitic transformation should occur, while the other regions are kept below Ac3 temperature to prevent martensitic microstructure development resulting in lower hardness but higher ductility. During the hot stamping process, the mechanical properties are adjusted by controlling the cooling conditions of the sheet with a locally heated tool [18]. According to Newton’s cooling law, a smaller temperature difference between the tool and the blank results in lower cooling rates and in a different microstructure development. Additionally, small gaps between the tool and the blank surface lead to hot spots, which are slowing down the cooling speed of the component, influencing the microstructure development, too [146]. Finally, an annealing operation after the conventional hot stamping process of the structural part is possible. The annealing process is promising regarding adjustment and controllability, but distortion of the component is possible and also an additional process step is needed [237]. All intrinsic strategies will result in a gradient of hardness of different extent between the different treated zones because of the inner heat conduction, which should be considered during dimensioning of the component. As an advantage, the notch effect of the weld seam of tailored welded blanks is avoided. Depending on the strategy, different tensile strengths and elongations are adjustable, as shown exemplarily in Fig. 33.

Fig. 31. Manufacturing of AHSS products for automotive industry. Fig. 33. Resulting mechanical properties in dependency of process strategy and used process parameter settings [143].

Fig. 32. Direct (a) and indirect (b) hot stamping process [145].

Tailored welded blanks include a welding step before the conventional hot stamping process to produce a blank out of two different materials [119]. A characteristic is the sharp gradient of hardness between the different steel grades, which can also be associated with negative effects like notching under certain load paths. If HSLA-steel is combined with boron steel, complex stamping products with a locally high degree of deformation are achievable [29]. Nevertheless, due to the thermal expansion and phase transformation a sequence of tension, compression and tension while quenching occurs, whereby inhomogeneous residual stresses and a complex springback behavior result. In order to predict the final geometry after cooling and unloading, a numerical analysis with coupled thermo-mechanical-metallurgical effects is suitable to map the stress-strain state in the press hardening process. Based on this model, the stress-strain state during phase transformation and cooling can finally be adapted through adjustment of the temperature-time history [29]. The concept of intrinsic tailored blanks is the adjustment of mechanical properties by influencing the time-temperature

Prediction of hardness in hot extrusion: In hot forward extrusion of aluminum alloys with secondary quenching, the process parameters such as extrusion ratio, punch speed, and temperature control the balance between the main forming mechanisms strain hardening and thermal softening [65]. Due to the complex characteristics, the recrystallization has been in the focus of several investigations [37,69]. The refinement of grains and deformation induced subgrains regulate finally the hardness of the product. Gu¨zel et al. investigated the dynamic grain structure evolution of EN AW-6082 alloy experimentally and numerically [81]. Obtained EBSD maps positioned on a flow path show the alternation of boundary misorientations besides the reduction of grain and subgrain sizes (Fig. 34a). The microstructure evolution results from a dynamic equilibrium, whereupon the continuous and geometric recrystallization softens the material in consequence to the poor nucleation of new grains and the low effect of discontinuous dynamic recrystallization [49,183]. The EBSD analysis shows, on each position aside from the initial state, the development of low angle grain boundaries beside in flow direction elongated grains. Other EBSD analyses show that both grain and subgrain size decrease for accumulating deformation, while the low angle boundaries are stagnating and high angle boundaries are rising. The mechanism for grain refinement is directly linked to the deformation driven high angle boundaries. To predict and control microstructural parameters, the simulated state-dependent variables are filled in relation laws. Sellars and Zhu [192] and Velay [219] described microstructural parameters in correlation to plastic strain and the temperature compensated Zener-Hollomon parameter. Thereby, an advanced

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

14

Fig. 34. (a) Analyzed microstructure by EBSD along a steady state flow line. (b) Computed grain and subgrain size over the equivalent plastic strain. (c) Vickers hardness measurement over subgrain size [81].

thermo-mechanical coupled simulation delivers the misorientations, grain and subgrain sizes in addition to state-dependent variables. With empirical equations, a connection between the hardness and the process parameters is drawn, see Section 3.2. Fig. 34b shows the grain and subgrain size as a function of equivalent plastic strains, whereupon increasing strains lead to a finer structure. After passing the orifice, a coarsening of grains due to static recrystallization is avoided by quenching [100]. Fig. 34c shows the Vickers hardness dependent on the subgrain size. To adjust the hardness and the subordinated yield or ultimate tensile strength, parameters like the temperatures and the punch speed represent simple actuating variables. In consequence to an actuating variable, the balance between strain-induced hardening and temperaturetime dependent softening as the final material properties is regulated. In addition to the hardness prediction, recent investigations focused on the numerical simulations enhance the accuracy [113], allow the weld prediction [76], and determine the grain size distribution [187].

dies (Fig. 35). In addition to the four times redirecting ECAP die, several modifications with variable angles and numbers of deflections, an inverse sequence and varying channel areas are imaginable, which allow multiple variations to control microstructure evolution. In every ECAP turn, an additional plastic strain is imposed, which can be calculated to etot  0.9 by using the upper bound method according to Eivani and Karimi for an ECAP turn angle of 908 and friction factor m = 1 [53]. As Fig. 36 shows, the ECAP extruded specimen reach a superior ductility for chip-based billets, which is 71% higher in comparison to flat-face extruded specimens. Nevertheless, the strength of all extrusions is on similar levels and not influenced by secondary parameters like temperature or ram speed. In relation to the hardness, no correlation to the tensile test results can be obtained. Further measurements demonstrate that the micro hardness intensely alternates with the inhomogeneous microstructure.

5.2. Product property: Formability In general, an increased hardness is accompanied by a reduced formability, as the previous thermo-mechanical processes revealed. With the objective of high ductility and strength at the same time, severe plastic deformation (SPD) processes are of great importance [15]. The examples of equal channel angular pressing (ECAP), continuous bending drawing (CBD), and accumulative roll bonding (ARB), make the significant influence of grain refinement on ductility visible. Control of formability in hot extrusion: The hot forward extrusion with integrated ECAP is used to recycle and shape aluminum chips to profiles with high quality mechanical properties in one step [82]. ECAP is a variant of the group of SPD processes, which enables a grain refinement with grain sizes below 1 mm through strong shear strains [15]. Extremely positive effects occur when ECAP is used in chip extrusion because a reliable solid state bonding of the chips is ensured. A feature of the ECAP extrusion is the multiple deflection of the chip based material with an upstream rising hydrostatic pressure, which is caused by the huge amount of shearing at every ECAP turn (Fig. 35). This high backpressure and the repeating shear strains allow defined bonding although low extrusion ratios are used. To control product properties, e.g., ductility, hardness, or strength, the process combination of forward extrusion and equal channel angular pressing can be modified by the sequence, numbers, and angles of deflection, respectively. Haase et al. [82] investigated the combined extrusion and ECAP of cast and chip-based billets with flat-face, porthole, and ECAP

Fig. 35. Hot extrusion of chip-based billets with different dies [82].

Fig. 36. Tensile test results of extrudates fabricated with different extrusion dies [83].

Paydar et al. [161,162] investigated the integration of ECAP upstream and downstream of forward extrusion, whereby an alternating ductility of the pure aluminum resulted. Tailored sheet metals by use of accumulative roll bonding: Accumulative roll bonding (ARB) belongs to the group of SPD processes [182]. The key idea of this processing strategy similar to cladding is the production of multi-layered sheet metal with a nanocrystalline grain structure. In Fig. 37a, the processing strategy is illustrated. Following a surface treatment for removing the oxide layer and to roughen the surface, the stacked sheets are joined in a

Fig. 37. (a) Scheme of the Accumulative Roll Bonding and a local heat treatment [182]. (b) Changes in mechanical values of 1100 pure aluminum and 6061 alloy with total equivalent strain in the ARB process [123].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

rolling operation. These steps are carried out several times until a certain number of layers and specific product properties are achieved, respectively. Huge shear deformations can be accumulated in the sheets during the rolling cycles. In general, an enhanced strength can be obtained due to work hardening and an increased dislocation density on the one hand and a reduced grain size on the other hand. The final properties depend, in particular, on the thickness reduction and the rolling temperature. Grain size and recrystallization effects, work hardening, and the bond strength are directly influenced by these two parameters. Fig. 37b shows the change of strength and elongation of two aluminum alloys with total equivalent strain etn, which is given by Eq. (4). In this equation n is the number of ARB cycles, h0 is the thickness of the initial specimen and hn is the thickness of the specimen after n cycles, [123]. 2

etn ¼ pffiffiffi ln 3

2n1 h0 hn

15

bending and unbending. An increase of ebending leads to an increase of the formability and a significant decrease of the flow stress, compare Fig. 39.

! (4) Fig. 39. Nominal stress-strain curves of Cu-0.3 wt%Sn alloy CBD specimens processes at equivalent bending strains (according to [228]). WD = wire drawing.

Amongst others, the ARB process can be applied to different aluminum alloys, titanium, copper, and steel. Moreover, the ARB process allows the combination of different metals or alloys such as the 5000 and 6000 series aluminum [90] and tailored alloying by means of particles such as copper between the different layers [188]. Regarding 6000 series aluminum alloys, there is a significant loss in ductility accompanying the increased strength. In this context, the Tailored Heat Treated Blanks (THTB) technology [222] was applied to nanocrystalline aluminum in order to locally regain the ductility and enhance the formability of the blank using a local short term heat treatment [144]. For nanocrystalline 6000 series aluminum besides the well-known dissolution of the precipitates, there is another softening effect, namely local recrystallization [133]. The combination of ARB and THTB allows the production of tailored sheet metal with a specific layer structure and property distribution optimized for the subsequent forming process and the final product properties. In this connection, property gradients in thickness direction and over the plane can be created. For the prediction of the properties after the roll bonding process, the FE simulation with a multi-scale approach (see Section 3.4) is used [189]. Concerning the design of subsequent forming processes, the Hill’90 criterion was identified as an applicable approach for modeling the mechanical behavior of ARB processed AA6016 [141]. Control of elongation through continuous bending drawing: Yanagimoto et al. [228] introduce a new method for the manufacturing of ultrafine electric wire called continuous bending drawing (CBD). Wires with diameters less than 0.3 mm combined with excellent electrical and mechanical properties are more and more requested by the automotive industry. The proposed process combines an SPD process (see [15]) and a draw bending process, allowing for the control of formability and strength by plastic deformation induced in different loading directions. Fig. 38 shows a schematic of the process. In order to control the bending radius at the last stand, the positions of the dies A, B, and C are flexible.

Fig. 38. Continuous bending drawing (CBD) process [228].

During the CBD process, a ‘deformation induced softening’ can be observed, depending on the equivalent bending strain ebending:

ebending ¼ lnð1 þ da =Rn Þ  ð1 þ db =Rn Þ

(5)

here Rn stands for the curvature radius of a neutral plane of the wire specimen and da and db are the radii of the specimen during

Thermal effects can be excluded as a reason for the softening. A qualitative explanation of the observed phenomenon is based on grain and dislocation structures of the material. Unlike during general plastic deformation, no sub-boundaries may be formed [85], but many incidental dislocation boundaries (IDBs) penetrate the elongated grains. The grains become elongated and the intervals between the IDBs increase. During draw-bending, the grains and dislocations are subject to compressive/tensile or tensile/compressive stresses in the longitudinal direction. The residual drawing stress may be reduced rapidly during reverse loading, resembling the Bauschinger effect observed in conventional cold forming, see Fig. 40, [228].

Fig. 40. Appearance of grain subdivision due to plastic deformation. (a) General plastic deformation. (b) Combined wire drawing and draw-bending process, [228].

5.3. Product property: Anisotropy The anisotropy has an influence on mechanical properties (Section 2.6) and is important for both the quality assurance of finished products and the defect-free forming. The following examples give a detailed insight of controlling textures in rolling and forging in order to enhance the functional characteristics. In the already mentioned ARB, besides strength and ductility, there is also a shift regarding the texture. The products reveal a strong texture with grains elongated in rolling direction and, furthermore, there are texture gradients in thickness direction due to repeated stacking. The lattice structure and the relation of r0, r45, and r90 change during the ARB cycles. With respect to thinning of the material in a deep drawing process, the sheets with higher number of ARB cycles are advantageous. In contrast, low-cycle sheets tend to show less earing [202].

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 16

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Texture control in rolling: Rolling is one effective instrument to influence the final texture, which is significant in most sheet forming processes to control the forming limits. The texture development within a process chain is influenced by the forming conditions, i.e., the temperature and strain, applied to a certain material. Every process step like casting, rolling, and heat treatment leads to a specific texture based on the process conditions and, therefore, affects the final product properties in a way that cannot be described by isotropic concepts like flow stress or a macroscopic failure criterion. A typical experiment incorporating the anisotropy of rolled material is cup drawing with earing. Several models (e.g., Taylor model, self-consistent method, crystal plasticity FEM) have been developed to cover this effect (compare Section 3) [17]. The major impact on the texture development during hot rolling is temperature and strain [150]. Fig. 41 shows the inverse pole figures of a stainless-steel hot strip sample in a thermomechanical plane strain compression test representing different finishing delivery temperatures during hot rolling in a tandem mill. It is obvious that different temperatures result in different textures. The scope of this study was to control the ridging phenomena, i.e., a surface defect in rolling direction. Here, lower finishing temperatures results in lower ridging height.

Fig. 41. Inverse pole figures in the core of a 430 stainless steel for different temperatures by thermo-mechanical compression. Given intensities are in multiple of random distribution [150].

To consider the influence of different strain states within the roll gap on texture, the rolling schedule has to be taken into account. Especially the shear profile depends on the geometry of the roll gap [195]. Fig. 42 shows the shear profile in the roll gap for different initial thicknesses. Consequently, the properties of a rolled product are not only determined by the rolled final geometry but also by the rolling schedule and the resulting texture.

Fig. 43. Rolling of aluminum EN AW-3104 (H19). (a) Aluminum beverage cans. (b) Distinct earing. (c) Reduced earing. (d) Measured earing profile with cold rolling direction (RD). (e) Simulated values. [59,92,156].

also captures the transformation from 08/908 earing to 458 earing at approx. 85%. The analysis consists of FEM computations, which are used to analyze the evolution of stresses and strains during the drawing process. The idealized stress/strain history is imposed on the textured material at different angles. The earing profiles are then computed from the variations in radial elongation under different in-plane angles [56]. Influence of fiber flow on static and dynamic properties of forged steel components: Another process example to control the textured mechanical properties is forging of steel. The mechanical properties of forged steel parts are regarded as advantageous compared with workpieces manufactured by machining or casting due to an uninterrupted fiber flow, which follows the surface contour in forged components. Schuster et al. prove that stretched manganese sulphides (MnS) are responsible for the fiber flow in sulfur-containing steels [190]. The shape and orientation of MnS has a strong influence on the impact toughness. The crack propagation in longitudinal impact toughness samples runs perpendicular to stretched MnS and requires much energy whereas the energy consumption of crack propagation along elongated MnS is much lower. Schuster et al. defined a factor of anisotropy AK according to Eq. (6), exposing the dependency of the notch impact energy on strain during cold upsetting and on the shape of MnS [190]. AK ¼

KV l KV t

(6)

KVl represents the measured impact energy of impact toughness samples oriented in rolling (longitudinal) or upsetting direction and KVt the impact energy transverse to these directions. Fig. 44 shows that an increase of the strain leads to a decrease of KVl while KVt changes only little, resulting in a lower value of AK in cold upset workpieces compared to rolled material.

Fig. 42. Grid distortion and net shear strain through the roll gap for thick plate (400 mm, 20% reduction) and thin plate (10 mm, 20% reduction)[195].

Control of anisotropy in aluminum sheet production: Standard beverage can bodies are made from aluminum sheets by a sequence of drawing processes. The efficient material usage is significantly influenced by the anisotropy of the sheet material since possibly occurring earing tips have to be clipped off. The amount of earing is controlled by the texture in the hot strip and by the degree of successive cold working [55]. Minimal earing after cold rolling can be achieved by an optimum combination of strength of the initial cube texture and rolling reduction. With a visco-plastic self-consistent polycrystalplasticity earing model (see Section 3.3), a very accurate prediction of the cup height profile is possible, see Fig. 43d and e. The model

Fig. 44. Dependency of notch impact energy and factor of anisotropy on strain [190].

After cold upsetting, MnS’s are completely crushed and reshaped, their influence on the fracture behavior is overlain by flattened grains of the steel microstructure (Fig. 45). In hot forged parts, flattened MnS with a very small nose radius can be responsible for crack initiation between the component and its burr, as shown in [173]. By the example of a common rail, the detrimental effect of extreme flattening of MnS under high strain is visible, Fig. 46. If the pressure in the rail rises, cracks form along the MnS leading to damage. The potential formation of flattened MnS in regions with elevated stress levels should be avoided and considered during design of components.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

17

Fig. 48. Residual stresses during ejection [211].

Fig. 45. Fracture surfaces of tested notch impact samples of 16MnCr5 at w = 0 (a and b) and w = 0.75 (c and d). Longitudinal (a and c) and transverse oriented samples (b and d). [190].

Fig. 46. Detail of a common rail (a), orientation of MnS (b) and shape of MnS (c) [190]. Fig. 49. Influence of the die stiffness on axial residual stresses during cold extrusion [211].

5.4. Product property: Residual stresses Residual stresses result from inhomogeneous plastic deformation over the cross-section of the formed workpiece. Their impact can be both positive and negative. On the one hand, they can lead to distortions or even failure when superposed by a service load [121]. On the other hand, compressive stresses at the surface have a positive effect on the fatigue behavior of cold forged parts [96]. Cold forming processes are particularly suitable for setting of different residual stress states. Control of residual stresses in cold forming processes: In processes such as reducing, forward rod extrusion, or wire drawing, residual stresses can be controlled via the strain, geometric parameters (e.g., die angle a), and stiffness of the tools, friction, and material characteristics [212]. Figs. 47 and 48 show that the residual stresses induced by the extrusion process are reduced by the small plastic deformation of the ejection stage. As shown in Fig. 49, the stiffness of the die influences the degree of reduction of the induced residual stresses [212].

Fig. 47. Residual stresses during extrusion [211].

5.5. Product property: Damage Damage accumulates with progressing cold plastic deformation in manufactured parts. This damage affects the service properties such as fatigue strength, static strength, and yield strength. Especially those defects which cannot be easily identified by non-

destructive testing should be avoided to prevent failure during the service life of the product. In the following, exemplary processes are presented in which defects are inhibited by a modification of the stress state, implemented by innovative tooling concepts. The damage accumulation in the interior of a bulk metal part and on the surface of a bent sheet were reduced. Damage reduction in cold forward extrusion: Material failure in the form of holes or cracks occur in cold forward extrusion when a critical point of damage is exceeded. These main defects are observed on the surface (snake-skin or fir-tree) or within an extruded part (chevron cracks or central bursts) depending on the process parameters such as reduction in area, die semi-cone angle, friction at the die and workpiece interface, working temperature, material ductility, etc. [203]. Although the surface cracks can be detected directly by standard surface inspection, the internal cracks require additional investigations, such as nondestructive ultrasonic testing. Internally occurring arrow-shaped central cracks result in a decrease in the load carrying capacity of the product. To investigate central crack formation mechanisms, several studies including experimental, analytical, and numerical methods were conducted [11,12,236]. These studies reveal that the positive triaxial stress state at the area reduction zone promotes the crack formation during plastic flow. Although low area reductions using high semi-cone angles enhance central damage accumulation, the friction motivates surface, rather than central, damage accumulation [203]. Without promoting the surface damage, superimposed hydrostatic pressure can be applied to modify the damage coupled plastic behavior of the material. While the effect of superimposed hydrostatic pressure differs with respect to material characteristics, it has been generally accepted that increased ductility and delay in nucleation, growth, and coalescence mechanisms for microvoids are gained utilities [61,164]. For certain die geometries and process parameters, the hydrostatic compression can be achieved by employing counter pressure at the extruded nose part of the workpiece. The experimental studies of [223] show that the application of counter pressure promotes material formability in extrusion so that it becomes possible to extrude even relatively brittle materials. Fig. 50 shows the calculated damage accumulation of an extruded part with consideration of friction, whereby a minimum counter pressure to

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 18

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Fig. 52. Micro-modeling based approach to access product life [199].

A variance-based Bayesian hierarchical modeling approach (see Section 3.5) of a hot bar rolling process is presented in [5]. This model allows the online control of material properties that affect surface defect formation in hot strip mills. Fig. 50. Effect of counter pressure on damage with consideration of friction (m = 0.04) [203].

avoid fractures could be determined [203]. Here, a counter pressure above 125 MPa was sufficient to decrease the damage accumulation below the critical value of 0.27 and avoid cracks. Counter pressure can be applied not only for the hindrance of the central crack but also for the reduction of damage accumulated in the product in order to increase further product life in service. Damage reduction in bending process: Experiments show that damage development in bending of modern alloys and polycrystals usually is motivated by intense strain localization at the free convex surface. Cracks initiate in the form of orange peels and lead to gradually growing undulations parallel to the bend axis [47]. As before, the Bridgman’s finding of higher ductility and delay in nucleation achieved by superimposed hydrostatic pressure allows the usage of compliant tools (e.g., PVC, PUR) for extended process limits. Application of additional controlled compression over the outer surface of a bent material with a counterholder lowers the circumferential stress at the outermost fiber of the material. Fig. 51 shows the damage according to Lemaitre in air bending, which is significantly reduced by counterpressure through elastomer tools [35]. Consequently, the fracture is prevented unlike in conventional bending processes [186]. The superposition of hydrostatic pressure can inhibit cracking during working of brittle materials [172].

5.6. Product property: Electromagnetic properties Electromagnetic properties such as magnetic flux density B and core losses W are important parameters of electrical steels. For a high effectiveness of electrical machinery cores, high B-values and low W values are aimed at. These electromagnetic material properties are determined by the microstructure (grain size and texture), and thus, can be controlled by suitable forming processes, e.g., rolling operations. In most cases, grain sizes between 80 and 120 mm and u- and hfibers are desirable, while a-, a*- and g-fibers result in unfavorable magnetic properties. For non-grain orientated (NO) electrical steels with high silicon content (no phase transformation), Sidor et al. [201] showed that the whole production chain, including slab reheating, hot rolling, cold rolling, and final recrystallization (RX), annealing affect the microstructure. In this investigation, electrical steel was hot rolled with different finishing temperatures, then cold-rolled and annealed. The different rolling conditions influence the hot rolled state as well as the properties after cold rolling and annealing. Fig. 53 shows the texture in the annealed state for 3.0 wt.% Si steel. In (a) the coiling temperature after hot rolling was 860 8C, while in (b) the temperature was 820 8C. Both texture and grain size (61 mm in (a) and 31 mm in (b)) differ, although, after hot rolling, the same cold rolling and annealing conditions were applied. On the basis of the microstructure properties the magnetic properties were calculated. For this calculation, the quality of texture is described by the minimum angle Au(g) between the applied field and the closest h1 0 0i direction of the crystal. The calculated values for B50 are similar, but the core losses W15/50 differ by approximately 10%, with 3.70 W/kg for (a) and 4.00 W/kg for (b).

Fig. 51. Numerical analysis of damage evolution in air bending without and with elastomer-tool [35].

This additional hydrostatic pressure makes all the principal stresses more compressive and hence reduces the tensile stresses below the critical value for cracking [33]. On the basis of this principle, an adjustment of the hardness of the elastomer counterholder is possible in order to control the superimposed stresses and to avoid crack occurence [36]. Modeling of damage during forging: An FEM based modeling approach which relates the process conditions during forging of titanium aeroengine disks to the mechanics of melt related failure (hard-alpha inclusions) is presented in [199]. The whole process chain from the billet to the final product is represented in a numerical framework, and the influence of each single forming step on the final material properties and the risk for failure (see Section 3.5) can be visualized and optimized regarding maximized durability, Fig. 52. An appropriate selection of process parameters and design of the forging sequence can significantly reduce the failure risk [4].

Fig. 53. Inverse pole figure of 3.0 wt.% Si steel after cold rolling and annealing. (a) Coiling temperature 860 8C. (b) 820 8C [201].

Similar results are shown by Lee et al. [122]. Cold rolling was performed in hot rolling direction and rotated by 908 to achieve different hot rolling textures. The texture was expressed by the ~ and correlated to the measured magnetic anisotropy parameter AðhÞ properties. The results show that an increasing anisotropy parame~ results in decreasing magnetic flux density and increasing ter AðhÞ core losses (Fig. 54).

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

19

Fig. 54. Magnetic flux density B50 (a) and core loss W15/50 (b) as function of anisotropy parameter Að~ hÞ [122].

6. Integration of manufacturing-induced properties in product design Metal forming technologies have inherent potentials for innovative products based on the product properties which are significantly influenced or modified during manufacturing processes by specific technologies (manufacturing-induced properties) [72]. Therefore, aside from the geometric properties, that are usually targeted in the design process, the microscopic and material properties need to be focused on as well to tap the full technological potential. Hence, one of the central challenges is to integrate these properties systematically into the product development process. 6.1. Classical product development and design approaches Classical product development and design approaches, e.g., Pahl and Beitz [60], VDI recommendation 2221 [218] and axiomatic design [205], recommend a stepwise procedure, starting from a product idea and the customer needs. Therefore, they are principally market-pull-oriented and do not include many aspects of a technology-push. Axiomatic design: Axiomatic design theory and methods focus, like other classical product design approaches, on meeting the customer needs by providing the product function. The designers’ task is described as a structured mapping over four dimensions between ‘‘what we want to achieve’’ and ‘‘how we want to achieve it’’. As shown in Fig. 55, manufacturing technologies are considered within the process domain, i.e., the last of the four domains. After the design parameters are chosen, appropriate manufacturing issues are considered in terms of process variables [205,206]. Modifications of the design parameters, based on specific manufacturing technology aspects, can be performed by iterations. The physical domain comprises a group of design parameters (Fig. 55 highlight (a)) that is achievable by geometric product design and can be manipulated in manufacturing by conventional process control. Further design parameters (Fig. 55, highlight (b)) are linked to material properties, especially to material property variation. Their provision requires a monitoring and process control of material properties. There is an underestimated need to focus on these design parameters. Defining an appropriate set of adjustable parameters allows to meet all functional requirements in a one-to-one mapping without exceptions.

Fig. 55. Four domains in axiomatic design [205].

VDI 2221: The recommended design process of VDI 2221 is structured in the phases of clarification of the task, conceptual design, embodiment, and detail design (see Fig. 56). As a basis for innovative and successful products, the early phases, especially the

Fig. 56. Iterations of Design for Manufacturing (DfM) in the product design process of VDI 2221 according to [72].

clarification of the task and the conceptual design phase, have to be intensified [52]. Important decisions are made in these phases, that influence the functionality, the costs, and the quality of the product. Because the focus is placed on the product function, the final conceptual design is only marginally affected by the manufacturing. Manufacturing technology aspects and information about the achievable product properties are incorporated only in later stages of the product design process, i.e., in the embodiment and the detail design [70,72]. Here, the manufacturing technologies are considered in terms of design rules and guidelines [60,177]. Design for Manufacturing: The basic idea of Design for Manufacturing (DfM), including also Design for Forming [8], is to feedback and to integrate information about the manufacturing technologies into the product design process. It must be carefully noted that this approach adapts the design process for easy manufacturing and not for deterministic superior product properties. Therefore, the manufacturing processes should be linked to the product design process. Reducing manufacturing time and costs as well as time-to-market constitute the targeted objectives of DfM. Thereby, the quality characteristics depending on manufacturing have to be ensured [60,140]. As shown in Fig. 56, DfM result in time consuming iterations that mainly affect the detail design and sometimes the embodiment design. The effect on determining the conceptual design is usually irrelevant [72]. A huge number of rules [60], guidelines [30], design references [177], advices and tools [140] exists in the literature. They support designers to generate a manufacturing-compliant solution. For example, DfM guidelines place emphasis on issues like process limits, advantageous part geometries, machine capabilities, energy consumption as well as ecological aspects. 6.2. New manufacturing integrated design approaches New product design approaches proposed by [72] expand existing approaches with special focus on the manufacturing technologies. The objective is to incorporate and to integrate the specific manufacturing-induced properties including the mechanical properties into the design process systematically. They use the characterization of manufacturing technologies by their manufacturing-induced properties to identify appropriate technologies for the specific design task at an early stage of the product design process. The selection of the manufacturing technology after the development task has been clarified allows the systematic integration of the manufacturing-induced properties into the further design process and reduces the number of iterations. The approach, shown in Fig. 57, is combined with mathematical optimization algorithms which support the goal-oriented exploitation of manufacturing-induced properties [72]. The procedure of property-mapping as the step to identify fitting manufacturing technologies and to integrate manufacturing-induced properties is described in [70]. In general, a product or

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 20

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

The systematic and comprehensive integration of manufacturing-induced properties allows a more detailed analysis of the product’s behavior and lifetime prediction. As a following step, the approaches can be expanded towards the entire life cycle in order to generate an integrated product (and process) development. These approaches are similar to [2,10,25], (Fig. 58), and focus on anticipating and influencing the entire process chain of the product life cycle during the product design process. 7. Summary and future perspectives

Fig. 57. Manufacturing integrated product design approach [72].

its elements have to be attributed to function-required properties, that are necessary to provide the product function. Propertymapping allows the identification of appropriate technologies by mapping the function-required properties on the respective manufacturing-induced properties systematically. This mapping allows to operate in both directions. On the one hand, starting from the function-required properties (market-pull), an appropriate manufacturing technology can be identified. On the other hand, starting from the manufacturing-induced properties, innovative product ideas can be systematically generated. Thus, the approach incorporates equally a technology-push. A case study in which a multi-functional linear guiding system has been developed illustrates the benefit of the approach. A process chain of linear flow splitting and linear bend splitting, coupled with subsequent roll-forming processes, is suitable to realize the function-required properties [72]. Because of the ultra-fine grained structure, linear flow split flanges, which are characterized by an increased hardness and ductile behavior at the same time [151], are predestined to be used as rolling contact surfaces. The manufacturing-induced properties go along with an increased rolling and sliding contact fatigue strength [106]. The selected technologies allow the manufacturing of bifurcations in a continuous process [71,73] and allow manufacturing of multi-chambered profiles in integral style [68]. In the case study, these chambers are used to provide an integrated breaking function by clamping the slide. With the aid of pressure generation, the side walls of two chambers are displaced outwards and used to generate a clamping force [72]. By manufacturing with the appropriate technologies, the physical product inherits the benefits of the manufacturinginduced properties and exploits the technological potentials in terms of realizing additional functional benefits and reducing weight, safety factors, or process steps, for example [70].

Fig. 58. Integrated product and process development [26].

The paper shows that metal forming, besides mere manufacturing of the desired shape, has the powerful ability of simultaneously setting desired mechanical and physical properties over the whole volume of the product. Today it is possible to set selected material parameters (e.g., ductility, strength, and residual stresses) of semi-finished products, like sheets and wires, in a homogenous manner. Fig. 59 summarizes the product properties and the process parameters, which are indirectly linked through the evolution of micro- and macrostructure. The control of properties is possible through the achievements of computational methods for predicting and controlling the properties (such as multi-scale or multi-physics modeling) as well as novel methods to measure the properties (e.g., based on optical, X-ray, or ultrasonic principles).

Fig. 59. Relationship between product properties and metal forming parameters.

Inhomogeneous setting and prediction of the properties is still at the beginning. In order to enhance the capabilities of predicting not only global, but local product properties of complex parts, the multi-scale methods must be improved with regard to precision and computation performance. Also the measurement methods to predict the in-volume properties need to be improved. Nevertheless, the presented developments mark the starting point of a paradigm change in the design of formed workpieces. Including this fact in the design process would allow a considerable reduction of safety factors of products and, hence, contribute significantly to the development of lightweight products that are environmentally benign. Current applications for predicting and setting product properties in metal forming can be considered as open-loop control of the product properties (Fig. 60a). It can be envisioned that, in the next decades, the paradigm shift will be accomplished by the final step of closed-loop control of the properties (Fig. 60b). The fundamentals of closed-loop control to adjust product properties will be discussed in the CIRP keynote 2016.

Fig. 60. (a) Open-loop and (b) closed-loop control of product properties. (CIRP keynote 2016).

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

Acknowledgments The authors wish to thank Prof. K. Osakada (Osaka University, Japan) for initiating the paper and all CIRP STC-F members for their valuable contributions during the discussions about its content. Mr. K. Isik and Mr. T. Clausmeyer (TU Dortmund University, Germany) are thanked for their support in preparation of the paper. The authors appreciate the thorough editing by Prof. D. Y. Yang and Prof. S. Smith, members of the Editorial Committee of the CIRP Annals. References [1] Abbaschian R, Abbaschian L, Reed-Hill R (2008) Physical Metallurgy Principles, 4th ed. Cengage Learning, Stamford. [2] Abele E, Anderl R, Birkhofer H, Ru¨ttinger B, (Eds.) (2007), EcoDesign: Von der Theorie in die Praxis, Springer, Berlin. [3] Agarwal K, Shivpuri R (2014) Knowledge Discovery in Steel Bar Rolling Mills Using Scheduling Data and Automated Inspection. Journal of Intelligent Manufacturing 25(6):1289–1299. [4] Agarwal K, Shivpuri R (2012) Hierarchical Decomposition Based Approach to Process Design of Aeroengine Disk in Presence of Defects. Transactions of NAMRI/SMEXL. [5] Agarwal K, Shivpuri R (2013) Online Prediction of Surface Defects in Hot Bar Rolling Based on Bayesian Hierarchical Modeling. Journal of Intelligent Manufacturing. http://dx.doi.org/10.1007/s10845-013-0834-y. [6] Allix O (2006) Multiscale Strategy for Solving Industrial Problems. Computer Methods in Applied Mechanics and Engineering 6:107–126. [7] Allwood JM, Cullen JM (2012) Sustainable Materials with Both Eyes Open, UIT Cambridge Ltd, Cambridge, England. [8] Altan T, Miller RA (1990) Design for Forming and Other Near Net Shape Manufacturing Processes. CIRP Annals—Manufacturing Technology 39(2):609– 620. [9] An Q, Hortig D, Merklein M (2012) Infrared Thermography as a New Method for Quality Control of Sheet Metal Parts in The Press Shop. Archives of Civil and Mechanical Engineering 12(2):148–155. [10] Andreasen MM, Hein L (1987) Integrated Product Development, Springer, Berlin. [11] Aravas N (1986) The Analysis of Void Growth that Leads to Central Bursts During Extrusion. Journal of the Mechanics and Physics of Solids 34(1):55–79. [12] Avitzur B (1968) Analysis of Central Bursting Defects in Extrusion and Wire Drawing. Journal of Manufacturing Science and Engineering Transactions of ASME 90(1):79–90. [13] Avitzur B (1979) Metal Forming: Processes and Analysis, Reprinted. Krieger Publishing Company, Huntington. [14] Avrami M (1939) Kinetics of Phase Change: I. General Theory. Journal of Chemical Physics 7:1103–1112. [15] Azushima A, Kopp R, Korhonen A, Yang DY, Micari F, Lahoti GD, Groche P, Yanagimoto J, Tsuji N, Rosochowski A, Yanagida A (2008) Severe Plastic Deformation (SPD) Processes for Metals. CIRP Annals—Manufacturing Technology 57(2):716–735. [16] Bahadur A, Kumar BR, Kumar AS, Sarkar GG, Rao JS (2004) Development and Comparison of Residual Stress Measurement on Welds by Various Methods. Materials Science and Technology 20(2):261–269. [17] Banabic D, Barlat F, Cazacu O, Kuwabara T (2010) Advances in Anisotropy and Formability. International Journal of Material Forming 3(3):165–189. [18] Bardelcik A, Ghavam K, George R, Worswick J (2011) An Impact Model of Hot Stamped Lab-Scale B-Pillar with Tailored Properties. in Oldenburg M, Steinhoff K, Prakash B, (Eds.) Hot Sheet Metal Forming of High-Performance Steel: 3, International Conference, Verlag Wissenschaftliche Scripten, Kassel, Germany221–228. [19] Bariani PF, Bruschi S, Ghiotti A, Turetta A (2008) Testing Formability in the Hot Stamping of HSS. CIRP Annals—Manufacturing Technology 57(1):265–268. ¨ ber die Vera¨nderung der Elastizita¨tsgrenze und des [20] Bauschinger J (1881) U Elastizita¨tsmoduls verschiedener Metalle. Civilingenieur 27:289–348. [21] Benammar A, Drai R, Guessoum A (2008) Detection of Delamination Defects in Cfrp Materials Using Ultrasonic Signal Processing. Ultrasonics 48(8):731– 738. [22] Van der Berg NG, Malherbe JB, Botha AJ, Friedland E (2012) Thermal Etching of SiC. Applied Surface Science 258(15):5561–5566. [23] Bhadeshia HKDH, Stone HJ (2009) Neural-Networks Modeling: Fundamentals of Modeling for Metal Processing. in Furrer DU, Semiatin SL, (Eds.) Fundamentals of Modeling for Metal Processing, ASM Handbook 22A, ASM International, Materials Park, OH, USA435–437. [24] Biermann D, Tekkaya AE, Tillmann W (2010) Tailor Made Properties—Visions for the Future of Manufacturing. in Biermann D, Tekkaya AE, Tillmann W, (Eds.) 1st International Conference on Product Property Prediction, TU Dortmund University, Dortmund, Germany77–91. [25] Birkhofer H (2011) From Design Practice to Design Science: the Evolution of a Career in Design Methodology Research. Journal of Engineering Design 22(5):333–359. [26] Birkhofer H, Rath K, Zhao S (2012) Umweltgerechtes Konstruieren: Handbuch Konstruktion, Carl Hanser, Munich. [27] Bland DR, Ford H (1948) The Calculation of Roll Force and Torque in Cold Strip Rolling with Tension. Proceedings of the Institution of Mechanical Engineers B: Journal of Engineering Manufacture 159:144–153.

21

[28] Blomme E, Bulcaen D, Declercq F (2002) Air-Coupled Ultrasonic NDE: Experiments in the Frequency Range 750 kHz–2 MHz. NDT & E International 35(7):417–426. [29] Bok HH, Choi JW, Suh DW, Lee MG, Barlat F (2014) Stress Development and Shape Change During Press-Hardening Process Using Phase-TransformationBased Finite Element Analysis. International Journal of Plasticity. http:// dx.doi.org/10.1016/j.ijplas.2014.11.004. [30] Boothroyd G (2002) Product Design for Manufacture and Assembly, 2nd ed. Marcel Dekker, New York, NY. [31] de Borst R (2008) Challenges in Computational Materials Science, Multiple Scales, Multi-Physics and Evolving Discontinuities. Computational Material Science 43(1):1–15. [32] Bouvier S, Haddadi H, Leve´e P, Teodosiu C (2006) Simple Shear Tests: Experimental Techniques and Characterization of the Plastic Anisotropy of Rolled Sheets at Large Strains. Journal of Materials Processing Technology 172(1):96–103. [33] Bridgman PW (1952) Studies in Large Plastic Flow and Fracture with Special Emphasis on The Effects of Hydrostatic Pressure, McGraw-Hill, New York, NY. [34] Bruschi S, Altan T, Banabic D, Bariani PF, Brosius A, Cao J, Ghiotti A, Khraisheh M, Merklein M, Tekkaya AE (2014) Testing and Modelling of Material Behaviour and Formability in Sheet Metal Forming. CIRP Annals—Manufacturing Technology 63(2):727–749. [35] El Budamusi M, Becker C, Clausmeyer T, Gebhard J, Chen L, Tekkaya AE (2015) Erweiterung der Forma¨nderungsgrenzen von ho¨herfesten Stahlwerkstoffen bei Biegeumformprozessen durch innovative Prozessfu¨hrung und Werkzeuge, Bericht zum Vorhaben IGF-Nr. 16585 N/FOSTA P930, eingereicht, Verlag und Vertriebsgesellschaft mbH, Du¨sseldorf. [36] El Budamusi M, Weinrich A, Becker C, Chatti S, Tekkaya AE (2014) Forming Limit Extension of High-Strength Steels in Bending Processes. Key Engineering Materials 611–612:1110–1115. [37] Byrne JG (1965) Recovery, Recrystallization, and Grain Growth, Macmillan, New York, NY. [38] Chaboche JL, Lesne PM (1988) A Non-Linear Continuous Fatigue Damage Model. Fatigue & Fracture of Engineering Materials & Structures 11(1):1–17. [39] Chao YJ, Ward Jr JD, Sands RG (2007) Charpy Impact Energy, Fracture Toughness and Ductile-Brittle Transition Temperature of Dual-Phase 590 Steel. Material and Design 28(2):551–557. [40] Chen L-C, Kao W-C, Huang Y-T (2006) Automatic Full-Field 3-D Profilometry Using White Light Confocal Microscopy With Dmd-Based Fringe Projection. Materials Science Forum 505–507:361–366. [41] De Chiffre L, Hansen HN, Kofod N (1999) Surface Topography Characterization Using an Atomic Force Microscope Mounted on a Coordinate Measuring Machine. CIRP Annals—Manufacturing Technology 48(1):463–466. [42] Chojecki H, Nehring J, Engels H, Mu¨ller-Bollenhagen C (2007) Ermittlung mechanisch-technologischer Eigenschaften warmumgeformter Automobilstrukturen durch die magnet-induktive Oberwellenanalyse, DGZfP Jahrestagung, Fu¨rth, Vortrag18. [43] Cockcroft MG, Latham DJ (1968) Ductility and the Workability of Metals. Journal of the Institute of Metals 96:33–39. [44] Conrad H (2000) Effects of Electric Current on Solid State Phase Transformations in Metals. Materials Science and Engineering A 287(2):227–237. [45] Cottrell AH (1967) An Introduction to Metallurgy, Edward Arnold Publishers Ltd, London. [46] D’Orazio T, Leo M, Distante A, Guaragnella C, Pianese V, Cavaccini G (2008) Automatic Ultrasonic Inspection for Internal Defect Detection in Composite Materials. NDT & E International 41(2):145–154. [47] Dao M, Lie M (2001) A Micromechanics Study on Strain-Localization-Induced Fracture Initiation in Bending Using Crystal Plasticity. Philosophical Magazine A 81(8):1997–2020. [48] Deshpande A, Shivpuri R, Ishikawa T (1995) Process—Structure Relationships in the Warm Forging of Microalloy Steels. Transactions of NAMRI/SME XXIII, 57–62. [49] Doherty RD, Hughes DA, Humphreys FJ, Jonas JJ, Juul Jensen D, Kassner ME, King WE, McNelley TR, McQueen HJ, Rollett AD (1997) Current Issues in Recrystallization: A Review. Materials Science and Engineering A 238(2):219–274. [50] Domblesky JP, Shivpuri R (1997) Grain Size Modeling and Optimization of Rotary Forged Alloy 718. Journal of Engineering Materials and Technology Transactions ASME 119:133–137. [51] Dutta B, Sellars CM (1998) Effect of composition and Process Variables on Nb(C, N) Precipitation in Niobium Microalloyed Austenite. Materials Science and Technology 3(3):197–206. [52] Ehrlenspiel K (2009) Integrierte Produktentwicklung: Denkabla¨ufe, Methodeneinsatz, Zusammenarbeit, Carl Hanser Verlag, Munich, Vienna. [53] Eivani AR, Karimi Taheri A (2008) The Effect of Dead Metal Zone Formation on Strain and Extrusion Force During Equal Channel Angular Extrusion. Computational Materials Science 42(1):14–20. [54] Elkhodary KI, Steven Greene M, Tang S, Belytschko T, Liu WK (2013) Archetype-Blending Continuum Theory. Computer Methods in Applied Mechanics and Engineering 254:309–333. [55] Engler O, Hirsch J, Kalz S (2006) Simulation of Sheet Anisotropy. in Hirsch J, (Ed.) Virtual Fabrication of Aluminium Products, Wiley-VCH, Weinheim189–198. [56] Engler O, Kalz S (2004) Simulation of Earing Profiles from Texture Data by Means of a Visco-Plastic Self-Consistent Polycristal Plasticity Approach. Materials Science and Engineering A 373:350–362. [57] Engler O, Karhausen K, Hirsch J (2009) Simulation of Microstructure and Texture Evolution in Aluminum Sheet. in Furrer DU, Semiatin SL, (Eds.) Fundamentals of Modeling for Metal Processing, ASM Handbook 22A, ASM International, Materials Park, OH, USA510–521. [58] Estrin Y, Mecking H (1984) A Unified Phenomenological Description of Work Hardening and Creep Based on One-Parameter Models. Acta Metallurgica 32(1):57–70.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 22

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

[59] European Aluminium Association. MATTER (2015) Beverage Cans. hhttp:// aluminium.matter.org.uk/content/html/eng/default.asp?catid=84&pageid=1941055071i. [60] Feldhusen J, Grote K-H(2013), Pahl/Beitz Konstruktionslehre: Methoden und Anwendung erfolgreicher Produktentwicklung, Springer, Berlin Heidelberg. [61] Filice L, Fratini L, Micari F (2003) Enhancing Formability of Aluminum Alloys by Superimposing Hydrostatic Pressure. Proceedings of Second MIT Conference on Computational Fluid and Solid Mechanics, 261–264. [62] Fishman GS (1996) Monte Carlo—Concepts, Algorithms and Applications, Springer, New York, NY. [63] Furrer DU, Semiatin SL (2009) Introduction to the Fundamentals of Modeling for Metal Processing. in Furrer DU, Semiatin SL, (Eds.) Fundamentals of Modeling for Metal Processing, ASM Handbook 22A, ASM International, Materials Park, OH, USA3–6. [64] Gawad J, Pietrzyk M (2007) Application of CAFE Coupled Model to Description of Microstructure Development During Dynamic Recrystallization. Archives of Metallurgy and Materials 52:257–266. [65] Van Geertruyden WH (2004) The Origin of Surface Recrystallization in Extrusion of 6xxx Aluminum Alloys, Lehigh University, Bethlehem, PA. [66] Gladman T (2002) The Physical Metallurgy of Microalloyed Steels, Maney, London. [67] Goidescu C, Welemane H, Garnier C, Fazzini M, Brault R, Pe´ronnet E, Mistou S (2013) Damage Investigation in CFRP Composites using Full-Field Measurement Techniques: Combination of Digital Image Stereo-Correlation, Infrared Thermography and X-ray Tomography. Composites Part B: Engineering 48:95–105. [68] Go¨rtan MO, Vucic D, Groche P, Livatyali H (2009) Roll Forming of Branched Profiles. Journal of Materials Processing Technology 209(17):5837–5844. [69] Gourdet S, Montheillet F (2000) Experimental Study of the Recrystallization Mechanism During Hot Deformation of Aluminium. Materials Science and Engineering A 283:274–288. [70] Gramlich S (2013) Vom fertigungsgerechten Konstruieren zum produktionsintegrierenden Entwickeln—Durchga¨ngige Modelle und Methoden im Produktlebenszyklus, VDI-Verlag, Du¨sseldorf. [71] Groche P, Ringler J, Abu Schreehah T (2009) Bending-Rolling Combinations for Strips with Optimized Cross-Section Geometries. CIRP Annals— Manufacturing Technology 58(1):263–266. [72] Groche P, Schmitt W, Bohn A, Gramlich S, Ulbrich S, Gu¨nther U (2012) Integration of Manufacturing-Induced Properties in Product Design. CIRP Annals—Manufacturing Technology 61(1):163–166. [73] Groche P, Vucic D, Jo¨ckel M (2007) Basics of Linear Flow Splitting. Journal of Materials Processing Technology 183:249–255. [74] Gro¨ger S, Dietzsch M, Gerlach M, Jeß S (2005) Real Mechanical Profile—The New Approach for Nanomeasurements. Journal of Physics: Conference Series 13:13–19. [75] Guillebert V, Nyingifa E, Frangolho D, Loureiro CC, Sporen A, Tinsley L, Shaw A, Redding LE, Roy R (2013) Development of a Thermographic NDI System for service Damage Identification in Inaccessible Areas. Procedia CIRP 11:124–129. [76] Gu¨ley V, Gu¨zel A, Ja¨ger A, Ben Khalifa N, Tekkaya AE, Misiolek WZ (2013) Effect of Die Design on the Welding Quality During Solid State Recycling of AA6060 Chips by Hot Extrusion. Materials Science and Engineering A 574:163– 175. [77] Gu¨ner A, Zillmann B, Lampke T, Tekkaya AE (2014) In-Situ Measurement of Loading Stresses with X-ray Diffraction for Yield Locus Determination. International Journal of Automotive Technology 15(2):303–316. [78] Gu¨ner A (2015) In-Situ Stress Analysis with X-ray Diffraction for Yield Locus Determination, Technische Universita¨t Dortmund, Dortmund. [79] Guo X, Vavilov V (2013) Crack Detection in Aluminum Parts by Using Ultrasound-Excited Infrared Thermography. Infrared Physics & Technology 61:149–156. [80] Gurson A (1977) Continuum Theory of Ductile Rupture by Void Nucleation and Growth: Part I—Yield Criteria and flow Rules for Porous Ductile Media. Journal of Engineering Materials and Technology Transactions ASME 99:2–15. [81] Gu¨zel A, Ja¨ger A, Parvizian F, Lambers HG, Tekkaya AE, Svendsen B, Maier HJ (2012) A New Method for Determining Dynamic Grain Structure Evolution During Hot Aluminum Extrusion. Journal of Materials Processing Technology 212(1):323–330. [82] Haase M, Ben Khalifa N, Tekkaya AE, Misiolek WZ (2012) Improving Mechanical Properties of Chip-Based Aluminium Extrudates by Integrated Extrusion and Equal Channel Angular Pressing (iECAP). Materials Science and Engineering A 539:194–204. [83] Haase M, Tekkaya AE (2015) Cold Extrusion of Hot Extruded Aluminum Chips. Journal of Materials Processing Technology 217:356–367. [84] Halder C, Madej L, Pietrzyk M (2014) Discrete Micro-Scale Cellular Automata Model for Modelling Phase Transformation During Heating Of Dual Phase Steels. Archives of Civil and Mechanical Engineering 14:96–103. [85] Hanazaki K, Shigeiri N, Tsuji N (2010) Change in Microstructure and Mechanical Properties During Deep Wire Drawing of Copper. Materials Science and Engineering A 527:5699–5707. [86] Hansen HN, Kofod N, De Chiffre L, Wanheim T (2002) Calibration and Industrial Application of Instrument for Surface Mapping Based on AFM. CIRP Annals—Manufacturing Technology 51(1):471–474. [87] Hara T, Tsuchiya K, Tsuzaki K, Man X, Asahata T, Uemoto A (2013) Application of Orthogonally Arranged FIB–SEM for precise Microstructure Analysis of materials. Journal of Alloys and Compounds 577:S717–S721. [88] Harri K, Guillaume P, Vanlanduit S (2008) On-Line Damage Detection on a Wing Panel Using Transmission Of Multisine Ultrasonic Waves. NDT & E International 41(4):312–317. [89] Hasegawa T, Yakou T, Karashima S (1975) Deformation Behaviour and Dislocation Structures Upon Stress Reversal in Polycrystalline Aluminium. Materials Science and Engineering 20:267–276.

[90] Hauso¨l T, Ho¨ppel H, Go¨ken M (2010) Tailoring Materials Properties of UFG Aluminium Alloys by Accumulative Roll Bonded Sandwich-Like Sheets. Journal of Materials Science 45(17):4733–4738. [91] Herlan T (1989) Optimaler Energieeinsatz bei der Fertigung durch Massivumformung, Springer, Berlin, New York, NY. [92] Hirsch J, Karhausen K, Wagner P (2000) Practical Application of Modeling in the Industrial Sheet Production. Materials Science Forum 331–337:421–430. [93] Hirt G, Kopp R, Hofmann O, Franzke M, Barton G (2007) Implementing a High Accuracy Multi-Mesh Method for Incremental Bulk Metal Forming. CIRP Annals—Manufacturing Technology 56:313–316. [94] Hocken RJ, Chakraborty N, Brown C (2005) Optical Metrology of Surfaces. CIRP Annals—Manufacturing Technology 54(2):169–183. [95] Hoffmann H, Hong S (2006) Tensile Test of Very Thin Sheet Metal and Determination of Flow Stress Considering the Scaling Effect. CIRP Annals— Manufacturing Technology 55(1):263–266. [96] Hoffmann JE, Lo¨he D (2002) Influence of Macro Residual Stresses on the Fatigue Behavior of Smooth and Notched Specimen Made from Quenched SAE 1045 Steel. HTM—Ha¨rterei Technische Mitteilungen 57(2):79–85. [97] Hofmann H, Mattissen D, Schaumann TW (2006) Advanced Cold Rolled Steels for Automotive Applications. Materialwissenschaft und Werkstofftechnik 37(9):716–723. [98] Humphreys FJ, Hatherly M (2004) Recrystallization and Related Annealing Phenomena, 2nd ed. Elsevier, Amsterdam. [99] Iadicola MA, Gna¨upel-Herold TH (2012) Effective X-ray elastic Constant Measurement for In Situ Stress Measurement of Biaxially Strained AA5754-O. Materials Science and Engineering: A 545:168–175. [100] Ja¨ger A, Gu¨zel A, Schikorra M, Tekkaya AE (2008) Production of Functionally Graded Aluminum EN AW-6082 Profiles by Extrusion with a Subsequent Quenching Strategy. Steel Research International Special Edition Metal Forming Conference 2(79):842–846. [101] Javadi Y, Sadeghi S, Najafabadi MA (2014) Taguchi Optimization and Ultrasonic Measurement of Residual Stresses in the Friction Stir Welding. Materials & Design 55:27–34. [102] Ji M, Shivpuri R (2006) Reduction of Random Seams in Hot Rolling through FEM Based Sensitivity Analysis. Materials Science and Engineering A 425:156– 166. [103] Jordan H-J (2007) Hochaufgelo¨ste beru¨hrungslose Oberfla¨chenmessung mit konfokal-optischen Messsystemen. 4. Symposium fu¨r beru¨hrende und beru¨hrungslose Oberfla¨chenmessung mit konfokal-optischen Messsystemen, Bamberg. [104] Kachanov LM (1986) Introduction to Continuum Damage Mechanics, Martinus Nijhoff Publishers, Dordrecht. [105] Karbasian H, Tekkaya AE (2010) A Review on Hot Stamping. Journal of Materials Processing Technology 210(15):2103–2118. [106] Karin I, Lommatzsch N, Lipp K, Landersheim V, Hanselka H, Bohn A (2012) Applications for a New Production Technology—Analysis of Linear Flow-Split Linear Guides. Proceedings of 11th Biennial Conference on Engineering Systems Design and Analysis (ESDA2012). [107] Kaye M, Puncreobutr C, Lee PD, Balint DS, Connolley T, Farrugia D, Lin J (2013) A New Parameter for Modelling Three-Dimensional Damage Evolution Validated by Synchrotron Tomography. Acta Materialia 61(20):7616–7623. [108] Kempf M, Skrabala O, Altsta¨dt V (2014) Acoustic Emission Analysis for Characterisation of Damage Mechanisms in Fibre Reinforced Thermosetting Polyurethane and Epoxy. Composites Part B: Engineering 56:477–483. [109] Kessler RW (2006) Prozessanalytik: Strategien und Fallbeispiele aus der industriellen Praxis, Wiley-VCH, Weinheim. [110] Kim H, Melhem H (2004) Damage Detection of Structures by Wavelet Analysis. Engineering Structures 26(3):347–362. [111] Kinashi H, Nagoshi T, Chang T-FM, Sato T, Sone M (2014) Mechanical Properties of Cu Electroplated in Supercritical CO2 Emulsion Evaluated by Micro-Compression Test. Microelectronic Engineering 121:83–86. [112] Kirstein S (1999) Scanning Near-Field Optical Microscopy. Current Opinion in Colloid & Interface Science 4:256–264. [113] Kloppenborg T, Schwane M, Ben Khalifa N, Tekkaya AE, Brosius A (2011) Experimental and Numerical Analysis of Material Flow in Porthole Die Extrusion. Key Engineering Materials 491:97–104. [114] Kobayashi S, Oh S-I, Altan T (1989) Metal Forming and the Finite-Element Method, Oxford University Press, USA, Oxford. [115] Kolleck R, Veit R, Merklein M, Lechler J, Geiger M (2009) Investigation on Induction Heating for Hot Stamping of Boron Alloyed Steels. CIRP Annals— Manufacturing Technology 58(1):275–278. [116] Kusiak J, Sztangret L, Rauch L, Pietrzyk M (2014) Metamodel Driven Optimization of Thermomechanical Industrial Processes. Computer Methods in Materials Science 14(1):20–26. [117] Kuziak R, Molenda R, Wrozyna A, Kusiak J, Pietrzyk M (2014) Experimental Verification and Validation of the Phase Transformation Model Used for Optimization of Heat Treatment of Rails. Computer Methods in Materials Science 14(1):53–63. [118] Kuziak R, Pietrzyk M (2011) Physical and Numerical Simulation of the Manufacturing Chain for the DP Steel Strips. in Hirt G, Tekkaya AE, (Eds.) Steel Research International, Special Edition ICTP 2011, Wiley-VCH, Weinheim756–761. [119] Lamprecht K, Deinzer G, Stich A, Lechler J, Sto¨hr T, Merklein M (2010) Thermo-Mechanical Properties of Tailor Welded Blanks in Hot Sheet Metal Forming Processes. in Kolleck R, (Ed.) IDDRG—50th Anniversary Conference; Tools and Technologies for the Processing of Ultra High Strength Steels: Conference Proceedings, Verlag der Technischen Universita¨t Graz, Graz37–48. [120] Landron C, Bouaziz O, Maire E, Adrien J (2013) Experimental Investigation of Void Coalescence in a Dual Phase Steel using X-ray Tomography. Acta Materialia 61(18):6821–6829. [121] Lange K (2008) Fliesspressen: Wirtschaftliche Fertigung metallischer Pra¨zisionswerkstu¨cke, Springer, Berlin, New York, NY.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx [122] Lee KM, Park SY, Huh MY, Kim JS, Engler O (2014) Effect of Texture and Grain Size on Magnetic Flux Density and Core Loss in Non-Oriented Electrical Steel Containing 3.15% Si. Journal of Magnetism and Magnetic Materials 354:324– 332. [123] Lee SH, Saito Y, Sakai T, Utsonomiya H (2002) Microstructures and Mechanical Properties of 6061 Aluminum Alloy Processed by Accumulative RollBonding. Materials Science and Engineering A 325:228–235. [124] Lemaitre J (1985) A Continuous Damage Mechanics Model for Ductile Fracture. Journal of Engineering Materials and Technology Transactions ASME 107:83–89. [125] Li Z, Grandhi RV, Shivpuri R (2002) Optimum Design of the Heat-Transfer Coefficient During Gas Quenching using the Response Surface Method. International Journal of Machine Tools & Manufacture 42:549–558. [126] Liewald M, Bolay C, Thullner S (2013) Shear Cutting and Counter Shear Cutting of Sandwich Materials. Journal of Manufacturing Processes 15(3):364–373. [127] Lu S, Li D, Brenner D (2014) Molecular Dynamics Simulations of Plastic Damage in Metals. in Voyiadjis G, (Ed.) Handbook of Damage Mechanics, Springer Science and Business Media, New York, NY453–486. [128] Madej L (2010) Development of the Modeling Strategy for the Strain Localization Simulation Based on the Digital Material Representation, AGH University Press, Krakow. [129] Madej L, Sieradzki L, Sitko M, Perzynski K, Radwanski K, Kuziak R (2013) Multi Scale Cellular Automata and Finite Element Based Model for Cold Deformation and Annealing of a Ferritic-Pearlitic Microstructure. Computational Materials Science 77:172–181. [130] Madej L, Mrozek A, Kus W, Burczynski T, Pietrzyk M (2008) Concurrent and Upscaling Methods in Multi Scale Modelling: Case Studies. Computer Methods in Materials Science 8:1–10. [131] Madej L, Wang J, Perzynski K, Hodgson PD (2014) Numerical Modelling of Dual Phase Microstructure Behavior Under Deformation Conditions on the Basis of Digital Material Representation. Computational Material Science 95:651–662. [132] Madej L, Hodgson PD, Pietrzyk M (2009) Development of the Multi-Scale Analysis Model to Simulate Strain Localization Occurring During Material Processing. Archives of Computational Methods in Engineering 16(3):287–318. [133] Maier V, Hauso¨l T, Schmidt C, Bo¨hm W, Nguyen H, Merklein M, Ho¨ppel H, Go¨ken M (2012) Tailored Heat-Treated Accumulative Roll Bonded Aluminum Blanks: Microstructure and Mechanical Behavior. Metallurgical and Materials Transactions A 43(9):3097–3107. [134] Martins PAF, Bay N, Tekkaya AE, Atkins AG (2014) Characterization of Fracture Loci in Metal Forming. International Journal of Mechanical Sciences 83:112–123. [135] Masuyama F (2010) Effect of Specimen Size and Shape on Creep Rupture Behavior of Creep Strength Enhanced Ferritic Steel Welds. International Journal of Pressure Vessels and Piping 87:617–623. [136] Mathia TG, Pawlus P, Wieczorowski M (2011) Recent Trends in Surface Metrology. Wear 271(3–4):494–508. [137] McVeigh C, Liu WK (2008) Linking Microstructure and Properties through a Predictive Multiresolution Continuum. Computer Methods in Applied Mechanics and Engineering 197(41–42):3268–3290. [138] McVeigh C, Liu WK (2010) Multiresolution Continuum Modeling of MicroVoid Assisted Dynamic Adiabatic Shear Band Propagation. Journal of the Mechanics and Physics of Solids 58(2):187–205. [139] Mecking H, Kocks UF (1981) Kinetics of Flow and Strain-Hardening. Acta Metallurgica 29(11):1865–1875. [140] Meerkamm H, Wartzack S, Bauer S, Krehmer H, Stockinger A, Walter M (2012) Design for X (DFX): Handbuch Konstruktion, Carl Hanser, Vienna. [141] Merklein M, Biasutti M, Nguyen H, Bo¨hm W (2011) Flow Behaviour of Advanced Aluminium Materials. in Hirt G, Tekkaya AE, (Eds.) Steel Research International, Special Edition ICTP 2011, Wiley-VCH, Weinheim1066–1071. [142] Merklein M, Lechler J, Geiger M (2006) Characterization of the Flow Properties of the Quenchenable Ultra High Strength Steel 22MnB5. CIRP Annals— Manufacturing Technology 55(1):229–232. [143] Merklein M, Lechler J, Sto¨hr T, Svec T (2010) Herstellung von funktionsoptimierten Bauteilen im Pressha¨rtprozess. Stahl und Eisen 6:51–57. [144] Merklein M, Vogt U (2009) Enhanced Formability of Ultrafine-Grained Aluminium Blanks by Local Heat Treatments. Proceedings of the SheMet, 169– 176. [145] Merklein M, Wieland M, Sto¨hr T, Lechler J, Gru¨ner M (2010) Analytic Methods for the Calculation of the Heat Transfer Coefficient. International Review of Mechanical Engineering 4:208–215. [146] Merklein M, Svec T (2013) Hot Stamping: Manufacturing Functional Optimized Components. Production Engineering 7(2–3):141–151. [147] Miehe C (2003) Computational Micro-to-Macro Transitions for Discretized Micro-Structures of Heterogeneous Materials at Finite Strains Based on the Minimization of Averaged Incremental Energy. Computer Methods in Applied Mechanics and Engineering 192(5–6):559–591. [148] Militzer M (2011) Phase Field Modeling of Microstructure Evolution in Steels. Current Opinion in Solid State and Materials Science 15:106–115. [149] Mitao S, Yanagimoto J (2006) Thermo-Mechanical Rolling. Handbook of Technology of Plasticity, Corona Publishing, Tokyo97–98. [150] Morimoto T, Yoshida F, Kusumoto Y, Oda M, Yanagimoto J (2012) Application of Recrystallization Texture Evolution Model to type 430 Stainless-Steel Strip Production. Materials Transactions 53:1837–1846. [151] Mu¨ller C, Bohn T, Bruder E, Landersheim V, El Dsoki C, Groche P, Veleva D (2007) Severe Plastic Deformation by Linear Flow Splitting. Materialwissenschaft und Werkstofftechnik 38(10):842–854. [152] Mu¨ller P, Cantatore A, Andreasen JL, Hiller J, De Chiffre L (2013) Computed Tomography as a Tool for Tolerance Verification of Industrial Parts. Procedia CIRP 10:125–132.

23

[153] Needleman A, Tvergaard V (1991) An Analysis of Dynamic Ductile Crack Growth in a Double Edge Cracked Specimen. International Journal of Fracture 49:41–67. [154] Neugebauer R, Altan T, Geiger M, Kleiner M, Sterzing A (2006) Sheet Metal Forming at Elevated Temperatures. CIRP Annals—Manufacturing Technology 55(2):793–816. [155] NN (2014) Advanced High-Strength Steels application guidelines Version 5.0, WorldAutoSteel. [156] Norsk Hydro ASA (2015) Beverage Cans. hhttp://www.hydro.com/en/Products/Rolled-products/Foil-and-strip-for-packaging/Beverage-can/i. [157] Ogun PS, Jackson MR, Parkin RM (2012) In-Process Surface Profile Assessment of Rotary Machined Timber Using a Dynamic Photometric Stereo. Journal of Systems and Control Engineering 226(6):823–830. [158] Ohara K, Tsugeno M, Imanari H, Sakiyama Y, Kitagoh K, Yanagimoto J (2014) Process Optimization for the Manufacturing of Sheets with Estimated Balance Between Product Quality and Energy Consumption. CIRP Annals— Manufacturing Technology 63(1):257–260. [159] Oyane M (1972) Criteria of ductile fracture strain. Bulletin of the Japan Society of Mechanical Engineers (JSME) 15:1507–1513. [160] Pauskar P, Shivpuri R (1999) Microstructure and Mechanics Interaction in the Modeling of Hot Rolling of Rods. CIRP Annals—Manufacturing Technology 48(1):191–194. [161] Paydar MH, Reihanian M, Bagherpour E, Sharifzadeh M, Zarinejad M, Dean TA (2008) Consolidation of Al Particles through Forward Extrusion-Equal Channel Angular Pressing (FE-ECAP). Materials Letters 62(17–18):3266–3268. [162] Paydar MH, Reihanian M, Bagherpour E, Sharifzadeh M, Zarinejad M, Dean TA (2009) Equal Channel Angular Pressing-Forward Extrusion (ECAP-FE) Consolidation of Al Particles. Materials & Design 30(3):429–432. [163] Peeters B, Kalidindi SR, Teodosiu C, Van Houtte P, Aernoudt E (2002) A Theoretical Investigation of the Influence Of Dislocation Sheets on Evolution Of Yield Surfaces in Single-Phase B.C.C. Polycrystals. Journal of the Mechanics and Physics of Solids 50(4):783–807. [164] Peng J, Wu PD, Huang Y, Chen XX, Lloyd DJ, Embury JD, Neale KW (2009) Effects of Superimposed Hydrostatic Pressure on Fracture in Round Bars Under Tension. International Journal of Solids and Structures 46(20):3741– 3749. [165] Persson U (2006) Surface Roughness Measurement on Machined Surfaces Using Angular Speckle Correlation. Journal of Materials Processing Technology 180(1–3):233–238. [166] Phadke S, Pauskar P, Shivpuri R (2004) Computational Modeling of the Phase Transformations and Mechanical Properties During Cooling of Hot Rolled Rod. Journal of Materials Processing Technology 150:107–115. [167] Pickering FB (1978) Physical Metallurgy and the Design of Steels, Applied Science Publishers, London. [168] Pietrzyk M (1990) Finite Element Based Model of Structure Development in the Hot Rolling Process. Steel Research 61:603–607. [169] Pietrzyk M (2002) Through-Process Modelling of Microstructure Evolution in Hot Forming of Steels. Journal of Materials Processing Technology 125– 126:53–62. [170] Po¨hlandt K, Tekkaya AE (1985) Torsion Testing—Plastic Deformation to High Strains and Strain Rates. Materials Science and Technology 1(11):972–977. [171] Psyk V, Risch D, Kinsey BL, Tekkaya AE, Kleiner M (2011) Electromagnetic Forming—A Review. Journal of Materials Processing Technology 211(5):787– 829. [172] Pugh H, Chandler EF, Holliday L, Mann J (1971) The Effect of Hydrostatic Pressure on the Tensile Properties of Plastics. Polymer Engineering and Science 11(6):463–473. [173] Raedt H-W, Herz M, Schuster A (2012) Gesteigerte Sicherheit bei der Auslegung von massivumgeformten Bauteil: Ausfa¨lle durch verformte Mangansulfide. Konstruktion 1(2):8–9. [174] Rauch EF, Schmitt J-H (1989) Dislocation Substructures in Mild Steel Deformed in Simple Shear. Materials Science and Engineering: A 113:441–448. [175] Roters F, Eisenlohr P, Hantcherli L, Tjahjanto DD, Bieler TR, Raabe D (2010) Overview of Constitutive Laws, Kinematics, Homogenization and Multiscale Methods in Crystal Plasticity Finite-Element Modeling: Theory, Experiments, Applications. Acta Materialia 58:1152–1211. [176] Roters F, Raabe D, Gottstein G (2000) Work Hardening in Heterogeneous Alloys—A Microstructural Approach Based on Three Internal State Variables. Acta Materialia 48(17):4181–4189. [177] Roth K (2000) Konstruieren mit Konstruktionskatalogen, Springer, Berlin, Heidelberg. [178] Roucoules C, Pietrzyk M, Hodgson PD (2003) Analysis of Work Hardening and Recrystallization During the Hot Working of Steel Using A Statistically Based Internal Variable Model. Materials Science and Engineering: A 339(1–2):1–9. [179] Roy S, Ghosh S, Shivpuri R (1996) Optimum Design of Process Variables in Multi-Pass Wire Drawing by Genetic Algorithms. Journal of Manufacturing Science and Engineering Transactions of ASME 118:244–251. [180] Roy S, Ghosh S, Shivpuri R (1997) A new Approach to Optimal Design of Multi-Stage Metal Forming Processes with Micro Genetic Algorithms. International Journal of Machine Tools and Manufacture 37:29–44. [181] Saeglitz M, Bake K, Gernert U (2010) Microstructure and Mechanical Properties in the Transition Zone of a Low Carbon Boron Steel After Partial Hardening. in Kolleck R, (Ed.) IDDRG—50th Anniversary Conference; Tools and Technologies for the Processing of Ultra High Strength Steels: Conference Proceedings, Verlag der Technischen Universita¨t Graz, Graz91–100. [182] Saito Y, Tsuji N, Utsonomiya H, Sakai T (1998) Ultra-fine Grained Bulk Aluminum Produced by Accumulative Roll-Bonding (ARB) Process. Scripta Materialia 39(9):1221–1227. [183] Sakai T, Miura H, Goloborodko A, Sitdikov O (2009) Continuous Dynamic Recrystallization During the Transient Severe Deformation of Aluminium Alloy 7475. Acta Materialia 57(1):153–162.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001

G Model

CIRP-1399; No. of Pages 24 24

A.E. Tekkaya et al. / CIRP Annals - Manufacturing Technology xxx (2015) xxx–xxx

[184] Sandstro¨m R, Lagneborg R (1975) A Model for Hot Working Occurring by Recrystallization. Acta Metallurgica 23(3):387–398. [185] Sathish S, Moran TJ, Martin RW, Reibel R (2005) Residual Stress Measurement with Focused Acoustic Waves and Direct Comparison with X-ray Diffraction Stress Measurements. Materials Science and Engineering: A 399(1–2):84–91. [186] Schiefenbusch J (1983) Untersuchungen zur Verbesserung des Umformverhaltens von Blechen beim Biegen, VDI-Verlag, Du¨sseldorf. [187] Schikorra M, Donati L, Tomesani L, Tekkaya AE (2008) Microstructure Analysis of Aluminum Extrusion: Prediction of Microstructure on AA6060 Alloy. Journal of Materials Processing Technology 201(1–3):156–162. [188] Schmidt C, Ruppert M, Ho¨ppel H, Nachtrab F, Dietrich A, Hanke R, Go¨ken M (2012) Design of Graded Materials by Particle Reinforcement during Accumulative Roll Bonding. Advanced Engineering Materials 14:1009–1017. [189] Schmidtchen M, Kawalla R (2010) Multiscale Modeling of Rolling Processes and Bond Strength Development for Layered Materials. Steel Research International 81(9):230–233. [190] Schuster A, Raedt H-W, Tekkaya AE (2013) Influence of Cold Upsetting on the Shape of the Microstructure and Inclusions in Different Kind of Steels and on the Notch Impact Energy. Proceedings of 46th ICFG Plenary Meeting. [191] Schwenke H, Neuschaefer-Rube U, Pfeifer T, Kunzmann H (2002) Optical Methods for Dimensional Metrology in Production Engineering. CIRP Annals— Manufacturing Technology 51(2):685–699. [192] Sellars CM, Zhu Q (2000) Microstructural Modelling of Aluminium Alloys During Thermomechanical Processing. Materials Science and Engineering A 280(1):1–7. [193] Sellars C (1980) The Physical Metallurgy of Hot Working. in Sellars C, Davies G, (Eds.) Hot Working and Forming Processes, Sheffield, England3–15. [194] Senkov ON, Miracle DB, Firstov SA, (Eds.) (2004), Metallic Materials with High Structural Efficiency. 146th ed. Kluwer Academic Publishers, DordrechtNATO Science Series II—Mathematics, Physics and Chemistry. [195] Seuren S, Seitz J, Kra¨mer A, Bambach M, Hirt G (2014) Accounting for Shear Deformation in Fast Models for Plate Rolling. Production Engineering 8(1– 2):17–24. [196] Sha XC, Li DZ, Zhang YT, Zhang XG, Li YY (2004) Modelling Effect of Hot Rolling Process Variables on Microstructure and Mechanical Properties of Low Carbon Strip Steels. Ironmaking & Steelmaking 31(2):169–175. [197] Shahtout MI, Younes MA, Ahmed MH (2012) Thermo-Mechanical Modeling of Thin Slab Direct Rolling of Nb Steels. Proceedings of the Institution of Mechanical Engineers B: Journal of Engineering Manufacture 226(8):1346– 1353. [198] Shiou F-J, Liu M-X (2009) Development of a Novel Scattered Triangulation Laser Probe with Six Linear Charge-Coupled Devices (CCDs). Optics and Lasers in Engineering 47(1):7–18. [199] Shivpuri R, Agarwal K (2010) The Role of Manufacturing Process in the Design for Product Risk. in Biermann D, Tekkaya AE, Tillmann W, (Eds.) 1st International Conference on Product Property Prediction, TU Dortmund University, Dortmund59–71. [200] Shivpuri R, Kini S (1997) Application of Fuzzy Reasoning Techniques for Roll Pass Design Optimization. Proceedings of the 39th Mechanical Working and Steel Processing Conference. [201] Sidor JJ, Verbeken K, Gomes E, Schneider J, Calvillo PR, Kestens LAI (2012) Through Process Texture Evolution and Magnetic Properties of High Si NonOriented Electrical Steels. Materials Characterization 71:49–57. [202] Skrotzki W, Hu¨nsche I, Hu¨ttenrauch J, Oertel C-G, Brokmeier H-G, Ho¨ppel HW, Topic I (2008) Texture and Mechanical Anisotropy of Ultrafine-Grained Aluminum Alloy AA6016 Produced by Accumulative Roll Bonding. Texture Stress and Microstructure 1–8. Article ID: 328754. [203] Soyarslan C, Tekkaya AE (2009) Prevention of Internal Cracks in Forward Extrusion by Means of Counter Pressure: A Numerical Treatise. Steel Research International 80(9):4–12. [204] Strnadel B, Hausild P (2008) Statistical Scatter in the Fracture Toughness and Charpy Impact Energy of Pearlitic Steel. Materials Science & Engineering A 486(1–2):208–214. [205] Suh NP (2001) Axiomatic Design: Advances and Application, Oxford Univ. Press, Oxford. [206] Suh NP (1998) Axiomatic Design Theory for Systems. Research in Engineering Design 10(4):189–209. [207] Szyndler J, Madej L (2015) Numerical Analysis of the Influence of Number of Grains, FE Mesh Density and Friction Coefficient on Representativeness Aspects of the Polycrystalline Digital Material Representation—Plane Strain Deformation Case Study. Computational Materials Science 96:200–213. [208] Tamura I, Sekine H, Tanaka OC, Ouchi C (1988) Thermomechanical Processing of High-Strength Low-Alloy Steels, Butterworth, London. [209] Tasan CC (2010) Micromechanical Characterization of Ductile Damage in Sheet Metal, TU Delft, Delft. [210] Tehranchi MM, Ranjbaran M, Eftekhari H (2011) Double Core Giant MagnetoImpedance Sensors for the Inspection of Magnetic Flux Leakage from Metal Surface Cracks. Sensors and Actuators A: Physical 170(1–2):55–61. [211] Tekkaya AE (1986) Ermittlung von Eigenspannungen in der Kaltmassivumformung, Springer, Berlin, New York, NY. [212] Tekkaya AE, Gerhardt J, Burgdorf M (1985) Residual Stresses in Cold-Formed Workpieces. CIRP Annals—Manufacturing Technology 34(1):225–230.

[213] Tekkaya AE, Ben Khalifa N, Grzancic G, Ho¨lker R (2014) Forming of Lightweight Metal Components: Need for New Technologies. Procedia Engineering 81:28–37. [214] Tekkaya AE, Lange K (2000) An Improved Relationship between Vickers Hardness and Yield Stress for Cold Formed Materials and its Experimental Verification. CIRP Annals—Manufacturing Technology 49(1):205–208. [215] Tian R, Chan S, Tang S, Kopacz AM, Wang J-S, Jou H-J, Siad L, Lindgren L-E, Olson GB, Liu WK (2010) A Multiresolution Continuum Simulation of the Ductile Fracture Process. Journal of the Mechanics and Physics of Solids 58(10):1681–1700. [216] Tsukada K, Yoshioka M, Kiwa T, Hirano Y (2011) A Magnetic Flux Leakage Method Using A Magnetoresistive Sensor for Nondestructive Evaluation of Spot Welds. NDT & E International 44(1):101–105. [217] Umemoto M (1994) Target of the Research of Microstructure-Mechanical Property Working Group: Prediction and Control of Deformation Property, Iron and Steel Institute of Japan (ISIJ), Tokyo. [218] Vdi-Fachbereich Produktentwicklung und Mechatronik (1993) Systematic Approach to the Development and Design of Technical Systems and Products, Beuth Verlag, Du¨sseldorf. [219] Velay X (2009) Prediction and Control of Subgrain Size in the Hot Extrusion of Aluminium Alloys with Feeder Plates. Journal of Materials Processing Technology 209(7):3610–3620. [220] Vernerey F, Liu WK, Moran B (2007) Multi-Scale Micromorphic Theory for Hierarchical Materials. Journal of the Mechanics and Physics of Solids 55(12):2603–2651. [221] Vollertsen F, Biermann D, Hansen HN, Jawahir IS, Kuzman K (2009) Size Effects in Manufacturing of Metallic Components. CIRP Annals—Manufacturing Technology 58(2):566–587. [222] Vollertsen F, Lange K (1998) Enhancement of Drawability by Local Heat Treatment. CIRP Annals—Manufacturing Technology 47(1):181–184. [223] Wagener HW, Haats J (1995) Crack Prevention and Increase of Workability of Brittle Materials by Cold Extrusion. Studies in Applied Mechanics: Materials Processing Defects 43:373–386. [224] Wang Z, Ishikawa T, Yukawa N, Kono A, Tozawa Y (1999) Computer Simulation and Control of Microstructure and Mechanical Properties in Hot Forging. CIRP Annals—Manufacturing Technology 48(1):187–190. [225] Wolfram S (1986) Theory and Application of Cellular Automata, World Scientific Press, Singapore. [226] Yan YJ, Cheng L, Wu ZY, Yam LH (2007) Development in Vibration-Based Structural Damage Detection Technique. Mechanical Systems and Signal Processing 21(5):2198–2211. [227] Yanagimoto J (2009) Numerical Analysis for the Prediction of Microstructure After Hot Forming of Structural Metals. Materials Transactions 50(7):1620– 1625. [228] Yanagimoto J, Tokutomi J, Hanazaki K, Tsuji N (2011) Continuous BendingDrawing Process to Manufacture the Ultrafine Copper Wire With Excellent Electrical and Mechanical Properties. CIRP Annals—Manufacturing Technology 60(1):279–282. [229] Yim S, Sonwalkar N, Saka N (1999) Molecular Dynamics Simulation of Boundary Lubricated Interfaces. Journal of Computer-Aided Materials Design 6(1):69–80. [230] Yin Q, Soyarslan C, Gu¨ner A, Brosius A, Tekkaya AE (2012) A Cyclic Twin Bridge Shear Test for the Identification of Kinematic Hardening Parameters. International Journal of Mechanical Sciences 59(1):31–43. [231] Yue ZM, Badreddine H, Dang T, Saanouni K, Tekkaya AE (2015) Formability Prediction of AL7020 with Experimental and Numerical Failure Criteria. Journal of Materials Processing Technology 218:80–88. [232] Yukawa N, Ishikawa T, Matsuo T, Nozaki Y (2011) Development of Virtual Laboratory System for Prediction of Microstructure and Mechanical Properties in Hot Forging of Vanadium Microalloyed Steel. in Hirt G, Tekkaya AE, (Eds.) Proceedings of the 10th International Conference on Technology of Plasticity, ICTP 2011, Verlag Stahleisen, Du¨sseldorf792–795. [233] Yusa N, Hashizume H, Urayama R, Uchimoto T, Takagi T, Sato K (2014) An Arrayed Uniform Eddy Current Probe Design for Crack Monitoring and Sizing of Surface Breaking Cracks with the Aid of a Computational Inversion Technique. NDT & E International 61:29–34. [234] Zeng D, Liu S, Makam V, Shetty S, Zhang L, Zweng F (2002) Specifying Steel Properties and Incorporating Forming Effects in Full Vehicle Impact Simulation. SAE Technical Paper 2002-01-0639, Society of Automotive Engineers, Warrendale, PA. [235] Zhang W, Shivpuri R (2008) Investigating Reliability of Variable Blank Holder Force Control in Sheet Drawing Under Process Uncertainties. Journal of Manufacturing Science and Engineering Transactions of ASME 130(4). 041001_1-041001_8. [236] Zimerman Z, Avitzur B (1970) Analysis of the Effect of Strain Hardening on Central Bursting Defects in Drawing and Extrusion. Journal of Manufacturing Science and Engineering Transactions of ASME 92(1):135–145. [237] Zimmermann F, Spo¨rer J, Volk W (2013) Partial Tempering of Press Hardened Car Body Parts by a Premixed Oxygen-Methane Flame Jet. in Oldenburg M, Prakash B, Steinhoff K, (Eds.) Hot Sheet Metal Forming of High-Performance Steel: 4, International Conference, Verlag Wissenschaftliche Scripten, Lulea, Sweden267–274.

Please cite this article in press as: Tekkaya AE, et al. Metal forming beyond shaping: Predicting and setting product properties. CIRP Annals - Manufacturing Technology (2015), http://dx.doi.org/10.1016/j.cirp.2015.05.001