Virtual process systems for part machining operations

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CIRP-1256; No. of Pages 21 CIRP Annals - Manufacturing Technology xxx (2014) xxx–xxx

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Virtual process systems for part machining operations Y. Altintas (1)a,*, P. Kersting b, D. Biermann (2)b, E. Budak (1)c, B. Denkena (1)d, I. Lazoglu (2)e a

Manufacturing Automation Laboratory, University of British Columbia, Canada Institute of Machining Technology (ISF), Technische Universita¨t, Dortmund, Germany Manufacturing Research Laboratory, Sabanci University, Istanbul, Turkey d Institute of Production Engineering and Machine Tools (IFW), Leibniz Universita¨t, Hannover, Germany e Manufacturing and Automation Research Center, Koc University, Istanbul, Turkey b c

A R T I C L E I N F O

A B S T R A C T

Keywords: Virtual Machining CAM

This paper presents an overview of recent developments in simulating machining and grinding processes along the NC tool path in virtual environments. The evaluations of cutter–part-geometry intersection algorithms are reviewed, and are used to predict cutting forces, torque, power, and the possibility of having chatter and other machining process states along the tool path. The trajectory generation of CNC systems is included in predicting the effective feeds. The NC program is automatically optimized by respecting the physical limits of the machine tool and cutting operation. Samples of industrial turning, milling and grinding applications are presented. The paper concludes with the present and future challenges to achieving a more accurate and efficient virtual machining process simulation and optimization system. ß 2014 CIRP.

1. Introduction The current trend is to develop digital models of the manufacturing chain from conceptual design to engineering analysis and manufacturing processes. Conceptual design has been practiced since the 1960s with the introduction of Computer Aided Design (CAD) and Computer Aided Manufacturing (CAM) methods. Engineering analysis has also accompanied design via Computer Aided Engineering (CAE) tools such as Finite Element (FE) Analysis. The concept of digital machines has also been widely implemented in industry by utilizing computer graphics and animation technologies. Machine tools are designed using solid models with integrated FE analysis systems that predict the mode shapes and their dynamic stiffness at the cutting tool–workpiece interface, which leads to a prediction of the machine’s maximum material removal limits during design [5]. The geometric removal of material on a machine tool is graphically simulated to check the collision and kinematic correctness of the tool path. Virtual geometric simulations of the material removal and machine tool motions are now commonly used in industry. The dynamics of the servo drives, trajectory generation, tool change and part handling mechanisms are simulated in virtual environments [15]. The interaction between the manufacturing processes and machine tools has also been analyzed using digital models as presented in [1,5,31]. However, the virtual machining of parts by considering the physics of the manufacturing processes has recently been evolving, and the progress being made in this field is subject of this keynote paper.

* Corresponding author.

The virtual machining concept is illustrated in Fig. 1. The CAD model of the part is used to generate NC programs in a CAM environment where the process planners design tool path strategies and select cutting conditions based on their experience. The NC program is tried on a physical machine, and if the process is found to be faulty, the trial and error cycle between the CAM and physical machining steps is repeated until a satisfactory result is obtained. The aim of the virtual machining is to reduce or even eliminate physical trials by simulating the physical operations in digital environments ahead of costly production as introduced by Altintas in 1991 [13]. There has been progress toward virtual machining by simulating the cutting forces and optimizing the feed along the tool path in three-axis peripheral [76,131] and three[74,91] to five-axis ball-end milling of dies and molds [104]. The virtual machining system requires sound mathematical models of metal cutting and grinding processes, the dynamics of machine kinematics and CNC servo drives, and cutter–part geometry engagement conditions along the tool path. The mechanics [20,82] and dynamics of cutting [16], drilling [123] and grinding [34,121] processes have been investigated for almost a century, and the progress in their mathematical modeling has been reported in the cited keynote papers and will not be repeated here. This paper presents the integration of cutting and grinding process models into CAM systems for the simulation of part machining operations in virtual environments. Henceforth, the paper is organized as follows. The identification methods for tool–workpiece engagement conditions along the tool path are summarized in Section 2. The computationally efficient mathematical modeling of metal-cutting and grinding process mechanics that are relevant to virtual machining are presented in

http://dx.doi.org/10.1016/j.cirp.2014.05.007 0007-8506/ß 2014 CIRP.

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Fig. 1. Architecture of a virtual machining system (UBC MAL).

Section 3. The kinematics and dynamics of machines that govern the relative motion between the tool and workpiece are given in Section 4. The optimization criteria for NC programs are given in Section 5 with the presentation of industrial applications in Section 6. The paper concludes by highlighting the current research challenges that need to be resolved before fully utilizing manufacturing process simulation and optimization tools in CAM environments. 2. Tool–workpiece-engagement identification algorithms Machining process simulation and optimization requires the geometric modeling of the engagement of the cutter with the workpiece at discrete intervals along the tool path [116,117]. The cutter–workpiece engagement (CWE) will lead to the variation in chip thickness, and axial and radial depth of cut which are needed to evaluate force [11,27,115], torque, power, vibration [98,99] and other process states along the tool path [68,134]. Various geometric modeling techniques are known in the literature for the description of the engagement between a tool and a workpiece, which are reviewed as follows.

However, the computation of the intersection between the represented surfaces (Fig. 2d) is a difficult and computationally time-consuming task. In contrast to the B-rep technique, the CSG (Constructive Solid Geometry) representation allows an easy description of the composition of individual components [72]. The idea of the CSG technique is to combine solid objects, e.g., spheres, cones, cuboids, using Boolean operations, like union, difference or intersection as shown in Fig. 3 [1,128].

Fig. 3. Example of a CSG-based composition: combining two primitives (here: cube and sphere) using set operations: union, difference, and intersection respectively (ISF).

2.1. Solid-model-based systems

2.2. Wire-frame-based systems

Solid modeling techniques, such as Constructive Solid Geometry (CSG) or Boundary Representation (B-Rep), are used to model three-dimensional objects [108]. These techniques were designed in the mid-1960s when CAD/CAM-systems required models containing the geometric dimensions of the parts. The method of describing solid objects by their boundaries, i.e., surface patches, edges and vertices, is called B-rep [26]. It supports various mathematical descriptions [77] such as Be´zier, Spline, or NURBS (NonUniform Rational B-Splines) techniques [72]. B-rep offers design flexibility and high reproducibility of free-form surfaces [26], and allows a continuous and accurate representation of the sweep volume [130] of a moving cutter envelope as shown in Fig. 2 [57,133].

The shape of a 3D object can also be represented by points and lines. These models do not provide any information about the inside and outside of the component, but allow a fast and simple visualization of the components as shown in Fig. 4, although not as smooth as in solid model representation of parts.

Fig. 4. Example of a wire-frame-based system: The tool and the workpiece are depicted by lines (ISF).

2.3. Voxel-, dexel-, and Z-buffer-based systems

Fig. 2. Example of a boundary representation of a tool-workpiece engagement. (a) Initial (P1) and final (P2) configuration of the cutter. (b) Sweep volume of the moving cutter envelope. (c) Raw stock material and generated sweep volume. (d) Result of the Boolean operation of the sweep volume and the raw stock material [133].

Modeling techniques based on z-buffer, dexels or voxels are discrete representations of objects [72]. Using a voxel-based system, the volume is approximated using small, uniform cuboids (Fig. 5) which are called voxels (volume element/volumetric pixel). Voxels are either filled with a material or kept empty. Since the number of voxels (n) depends on the resolution by O(n3) [128], the drawback of this easy-to-implement modeling technique is a high demand of memory and computation time when the resolution of the model is increased.

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Fig. 7. Concept of the dexel-based technique (ISF).

2.4. Point-based methods

Fig. 5. Example of a milling simulation using a voxel-based workpiece model [119].

The basic idea of dexel (depth element)-based systems is to discretize an object not by using cuboids, but parallel line segments, which are arranged on a regular grid. These line segments can have different lengths defined by their start and end points. If the elements are only used in one direction and share the same start value [73,134], the method is called zbuffer technique (Fig. 6), which is named after the rendering technique in graphic processor units [129]. This approach is very easy to implement and has a low computation time. However, only convex shapes (without undercuts) can be modeled, and each line has to be replaced by a list of line segments [128].

Point-based methods discretize an object using single points (Fig. 8 [85]). For example, the basis for element-free techniques, like SPH (Smooth Particle Hydrodynamics [93]), or methods using background meshes, e.g., MPM (Material Point Method [19]) are used. These techniques are adapted from computer science in order to overcome the disadvantages of finite element models, e.g., a strong distortion of finite elements resulting in a low computation accuracy or high computational costs for re-meshing [19]. 2.5. Analytical methods Besides discretization and solid modeling techniques, analytical approaches have also been used in calculating tool–workpiece engagement. Such approaches mainly depend on the approximation of the workpiece surface information by use of the cutter location (CL) file itself as shown in [126]. The cutting parameters such as step over, cutting depth, lead and tilt angles are calculated using the analytical difference between consecutive CL points, tool axis vector and the approximated workpiece surface (Fig. 9). Although such methods are fast, their application is limited to special machining cases such as ball end milling of blades due to the geometrical definition requirements for the features on the part. 2.6. Discussions

Fig. 6. Example of modeling the surface of a honing tool [83] using z-buffer method (ISF).

In order to improve the accuracy of the z-buffer model, three dexel models – each aligned along one of the Cartesian axes – can be used (Fig. 7). In contrast to the voxel-based technique, the dexel model has less memory demand of O(n2).

The choice of the models for the workpiece and the tool (Fig. 8) depends on the focus of the simulation, the required accuracy of the results and the demand on the computation time. Additionally, if particular simulation systems or commercial software are used, the kind of model is generally predetermined. The approach for calculating the material removal process and for the tool– workpiece-engagement identification depends on the chosen models. Generally, the machining processes are simulated using a time [133] or displacement discretization of the tool path [97,115] (Fig. 10).

Fig. 8. Different possibilities to model the workpiece and the tool (ISF).

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equation: 0

1

B C B W0 C C \ Tn Cn ¼ B Bn1 C @[ A Ti

(1)

i¼1

where W0 is the model of the stock material and Ti is the envelope of the tool model at the position of the nth cut (Fig. 12 [118]). Since Boolean operations can be defined easily and accurately, the CSG technique is a popular model for high-precision engagement calculations [85].

Fig. 12. Geometric model of the current chip shape Cn based on the CSG technique (ISF). Fig. 9. Analytical calculation of cutting parameters [105].

The visualization of the material removal on the part is also an essential feature in virtual machining. Although a CSG representation of the workpiece can be updated in constant time, it is computationally costly to directly render this model using raytracing techniques [115]. Therefore, an alternative visualization model is often used in combination with the CSG model [118]. 3. Metal cutting process models

Fig. 10. Discretization of the tool movement [133]. (a) Tool at different NC positions. (b) Approximated sweep surface.

Since discrete models (voxel-, dexel-, z-buffer-based systems) are easy to implement, they are most commonly used for the representation of tools or workpieces [24,25,28,56]. The simplest and fastest approach is the z-buffer technique, which is, for example, used in (Fig. 6) to model the honing process [83]. However, z-buffer models are insufficient for use in the five-axis machining of free-form surfaces, where dexel- or voxel-based systems are preferred (Fig. 11). Due to the discrete sampling, these modeling techniques are subject to aliasing errors, which can be reduced by increasing the number of elements (dexel or voxel) but at the expense of higher computation loads and larger memory demands, i.e. O(n3) for voxel and O(n2) for dexel boards. If a more precise model is needed, solid modeling techniques such as CSG or B-rep methods, should be used [92].

Fig. 11. Example of dexel-based workpiece and a CSG-based tool model. Simulation of the (a) NC-milling process, (b) NC-grinding, (c) drill grinding (ISF).

Using the CSG technique, the material removal process and the current chip shape Cn can be easily described by the following

The main objective in simulating part machining operations in a virtual environment is to predict the maximum cutting forces, torque, power, vibration amplitudes and dimensional surface errors left on the part in short computational time windows. As a result, the micro-mechanics of cutting which aims to predict the temperature, stress, residual stresses and strain distribution in tool–chip interface zones are not considered in the virtual machining of parts. However, the micro-analysis at the cutting zone is still possible by freezing the tool at a particular location and simulating the process in detail if needed. The details of micromechanics models, which are not used by NC programmers but by process design specialists, can be found in the past CIRP keynote papers [20,82]. 3.1. Mechanics of orthogonal cutting The macro mechanics of orthogonal cutting can be simplified by assuming a thin shear plane with an average shear angle (fc) and shear yield stress (ts) as shown in Fig. 13 [4]. The sticking and sliding friction zones between the chip and rake face are simplified by having an average Coulomb friction coefficient (ma). The fundamental orthogonal cutting parameters (fc, ts, ma) can be obtained from orthogonal cutting tests and stored in a material data base as an experimentally calibrated function of uncut chip thickness (h), cutting velocity (V) and tool rake angle (gr) [4]. Alternatively, the orthogonal cutting parameters can be predicted from micro-metal cutting mechanics methods such as Finite Element or slip line field analysis [136,137]. The chip thickness (h) has a static part (hcs) which is dependent on the tool geometry and kinematics of the cutting operation, and a dynamic part (hcd) due to regenerative vibrations [9,16,37]. Since a number of metal cutting operations such as turning, drilling, and milling occur in machining a part on CNC machining centers, the mechanics of cutting for a variety of tool geometries must be handled by the virtual machining system. The cutting edge

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Fig. 13. Mechanics of orthogonal cutting with thin shear plane [4].

geometry is first defined according to ISO 3002 [81] standards. The normal rake, cutting edge and oblique angles, which are needed in modeling the mechanics of cutting, are evaluated from the tool geometry and cutting velocity direction [84]. The tool reference plane (Pr) is oriented perpendicular to the primary motion (cutting velocity) vector and parallel to the tool axis (Fig. 14a). The tool cutting edge plane (Ps) is tangential to the cutting edge at the selected point and perpendicular to Pr. The cutting velocity vector (v0) is transformed to design coordinates (Frame D) after the insert is oriented on the cutter body (Fig. 14b) [84]. The tool with an oblique angle of cut (ls) and the normal rake angle (gn) is defined in the oblique cutting mechanics frame (Fig. 14) as a function of the orientation of the cutting edge with respect to the cutting velocity direction and the cutter body. The details of geometric transformations can be found in [84]. The generalized mechanics model requires the evaluation of the friction force on the rake face (Fu), which is aligned with the chip flow angle (h) and the normal force (Fv) described in chip flow coordinates, as shown in Fig. 15. A differential cutting edge that produces a chip with an area (dAc) and length (dS) creates the following friction and normal forces: dF u ðkdzÞ ¼ K uc ðkdzÞdAc ðkdzÞ þ K ue dSðkdzÞ dF v ðkdzÞ ¼ K vc ðkdzÞdAc ðkdzÞ þ K ve dSðkdzÞ

Fig. 14. Tool-in-hand planes and motion directions (adapted from ISO 3002) and the definition of normal rake angle [81].

(2)

where Kuc and Kvc are the friction and normal cutting force coefficients, and Kue and Kve are the edge force coefficients in oblique cutting for each insert and differential segment (k) with dz height. The chip area and width are evaluated as dAcj ðkdzÞ ¼ h j ðkdzÞ  dS j ðkdzÞ

and

dS j ðkdzÞ ¼

dz sin kr j

where hj (kdz) is the local chip thickness cut by tooth j. Cutting coefficients in friction and normal directions can be evaluated using orthogonal to oblique transformation methods such as proposed by Armarego et al. [21] as follows: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

t s 1  tan2 h sin2 bn

qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi sin ba ; cos2 ðfn þ bn  g n Þ þ tan2 h sin2 bn qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi t s 1  tan2 h sin2 bn qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi cos ba : ¼ cos ls sin fn cos2 ðfn þ bn  g n Þ þ tan2 h sin2 bn

K uc ¼

cos ls sin fn

K vc

(3)

Fig. 15. Mechanics of oblique cutting [84].

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The shear stress (ts), shear angle (fn), average friction angle (ba = tan1 ma) and edge coefficients (Kue and Kve) are evaluated from the orthogonal cutting model as described in [21]. The projection of the friction angle on the cutting edge normal plane is shown as (Pn), is bn = tan1(ms cos h), where h is the chip flow angle which can be assumed to be equal to oblique angle h  ls using the Stabler rule [48] for simplistic force analysis. Alternatively, the cutting force coefficients (Kuc, Kvc) can be mechanistically calibrated from dedicated cutting tests conducted with each tool geometry. Both methods are widely used in creating a cutting force coefficient data base for various materials. The edge force coefficients (Kue, Kve) are highly dependent on the radius of the cutting edge and flank wear. They need to be identified either experimentally, or by using Finite Element [20,137] or slip line field [136] models. The thermo-mechanical behavior of the material can be modeled using the Johnson–Cook material model where strain, strain rate and temperature effects can be considered in Finite Element or slip line field models when predicting the cutting force coefficients [38,101]. A sample mechanistic cutting force coefficient identified from the flow stress and friction parameters of a material as a nonlinear function of chip thickness (h) and cutting edge radius (r) from Finite Element and slip line field simulations is given as [17]: d

p

K t ðh; r Þ ¼ K t1 ðhÞ þ K t2 ðh; r Þ ¼ at h t þ bt h t r qt :

Fig. 16. Turning operation [9].

(4)

Once the differential friction (dFu) and normal (dFv) forces on the rake face are identified from the material properties and cutting edge geometry (Eqs. (2) and (3)), they can be integrated along the width of cut (b) to find the total cutting (Fu, Fv) forces on the rake face of the cutting edge. 3.2. Transformations to machining coordinates In a virtual machine tool, the chip thickness must be calculated as a function of feed, tool geometry, the kinematics of the machining operation, and relative vibrations between the cutting tool and workpiece. There are several coordinate systems in the process chain, but the fundamental base is the rake face of the tool where the chip leaves the part. The rake face coordinates (u, v) are transformed into cutting edge coordinates (x, y, z-RTA coordinate system I) from Fig. 15 as [84]: fx

z gTI ¼ T1U f u

y

v gT

Fig. 17. Multiple teeth boring [84].

(5)

2

T 1U

3 cosðg n ÞcosðhÞ sinðg n Þ ¼ 4 sinðls ÞsinðhÞ þ cosðls Þsinðg n ÞcosðhÞ cosðls Þcosðg n Þ 5: cosðls ÞsinðhÞ þ sinðls Þsinðg n ÞcosðhÞ sinðls Þcosðg n Þ

The RTA coordinates define the cutting edge, which can be transformed to a machine coordinate system (0) as a function of each machining operation:   T0R  TRI (6) f x y z c gT0 ¼ f x y z gTI 0 Rt 0 where Rt is the moment arm for the force in y direction creating the torque in direction c. The transformation matrices TOR and TR1 are unique for each machining operation. TOR matrix is represented as: 2 3 sin ðc j Þ cos ðc j Þ 0 4 (7) TOR ¼ cos ðc j Þ sin ðc j Þ 0 5; 0 0 1 where the location angle (cj) and TR1 depend on the cutting tool orientation for each operation as shown in Figs. 16–18. The chip thickness is measured perpendicular to the cutting edge and defined by the following general expression: h j ðt; kdzÞ ¼ c j sin k

 r ðkdzÞ

 sin f j ðt; kdzÞ þ

ecd j ðt; kdzÞ

 DqðtÞ

(8)

Fig. 18. Drilling operation [9].

where cj is the feed rate for tooth j. The engagement angle time varies with the time in milling (fj = cj) and the constant for turning (fj = p/2) operations.

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The vibrations in lateral (x, y) and axial (z) directions are defined in machine coordinates as [9]: qðtÞ ¼ f xðtÞ

zðtÞ gT

yðtÞ

(9)

The effect of regenerative vibrations ðDq ¼ qðtÞ  qðt  TÞÞ is considered by orienting them into the direction of the chip thickness with the transformation vector: e j ðt; kdzÞ ¼ f 1

T

0

0 gðT0R  TR1 Þ :

(10)

The cutting forces on the rake face (Eq. (2)) are transformed to RTA coordinates using Eq. (5) which have cutting, ploughing and process damping parts as follows [59]: ed dF j ðt; kdzÞ ¼ dFcj ðt; kdzÞ þ dFes j ðt; kdzÞ þ dF j ðt; kdzÞ ; |fflfflfflfflfflfflffl{zfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} |fflfflfflfflfflfflfflffl{zfflfflfflfflfflfflfflffl} cutting

Fðt; kdzÞ ¼



Fx

Fy

Fz

Tc

T

ploughing N X K X

process damping

(11)

7

3.3.2. Boring heads and drilling operations Drilling and boring heads have multiple cutting edges but with identical kinematics as shown in [22,23]. The tool rotates and moves axially along the hole axis, and the corresponding transformation parameters are set as shown in Figs. 17 and 18 [9,84]. 3.3.3. Milling operations Milling cutters have multiple teeth but the direction of feed is perpendicular to the plane formed by the spindle (Z8) and normal to the feed (X8) directions, as shown in Fig. 19. If the cutter has an arbitrary geometry with different inserts along each flute as in Fig. 20, the cutter is subdivided into small differential disk elements [7,10]. The contributions of all differential cutter elements to forces are calculated and summed digitally to find the total forces, torque and power contributed by the total cutter engaged with the workpiece [60,61].

gðt; kdzÞdF j ðt; kdzÞ;

¼ 0

j¼1 k¼1

where g (t, kdz) = 1 when the cutting edge is in cut and g (t, kdz) = 0 otherwise. The cutting component ðdF cj Þ of the forces is divided into cd stationary ðdF cs j Þ and dynamic ðdF j Þ parts as: ðt; kdzÞ þ dFcd ðt; kdzÞ; dFcj ðt; kdzÞ ¼ dFcs j   j T0R  TRI cs dF j ðt; kdzÞ ¼ T 1U 0 Rt ðkdzÞ 0  K uc ðkdzÞ c j sinf j ðt; kdzÞ dz; K vc ðkdzÞ   T0R  TRI ðt; kdzÞ ¼ T dFcd j 0 Rt ðkdzÞ 0 1U  K uc ðkdzÞ ecd j ðt; kdzÞDqðtÞ dz: K vc ðkdzÞ

(12)

The ploughing forces are given as:   T0R  TRI ðt; kdzÞ ¼ T1U  dFes j 0 Rt ðkdzÞ 0  1 K ue ðkdzÞ dz K ve ðkdzÞ sinðkr ðkdzÞÞ

(13)

Fig. 19. Milling operation [9].

Process damping forces are found as [9]: dFed j ðt; kdzÞ ¼



T0R  TRI 0 Rt ðkdzÞ

 0

e pd K s p Lw 2 j ðt; kdzÞ dz P 4vc sinðkr ðkdzÞÞ

(14)

where P ¼ f 1 m 0 gT and m is the Coulomb friction coefficient. Ksp is the material-dependent contact force coefficient; Lw is flank wear land width of the tool and vc is the cutting speed. The total dynamic forces that affect the stability of cutting become: Fd ðtÞ ¼ Fcd ðtÞ þ Fed ðtÞ:

(15)

The effects of run-out, variable pitch and helix, and serrated edge can also be added to the overall cutting forces [59,95]. 3.3. Specific machining operations Fig. 20. Cutters with arbitrary geometry [10].

The generalized force models can be adapted to various chip removal operations by simply transforming the forces from RTA (cutting edge) coordinates to the coordinates of the specific machining operation. Only the operation and tool-geometry specific engagement angle (c) and transformation matrices (TR1,e) are needed as described in Figs. 16–18 [9]. 3.3.1. Single point turning operations Single point turning and boring operations have the same geometry, and the fundamental parameter is the side cutting edge angle ðkr Þ as shown in Fig. 16. The rotational matrices TOR and TRI are automatically transformed to predict cutting forces in turning.

An example of simulated milling forces predicted by considering the thermo-mechanical properties of the material is shown in Fig. 21 [38]. Instead of simulating the periodic states such as milling forces and torque at discrete time intervals, Altintas et al. [11] argued that the maximum force, torque, power and dimensional surface errors are the most crucial information for process planners. The maximum values of the process states must not exceed the machine limits or the tolerance of the part. They developed analytical, closed form formulas to predict the maximum and minimum values of process states at each discrete

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Fig. 22. Time domain milling simulation. (a) Visualization of surface location errors during the simulation of the machining process. (b) Analyzing the chip shape using scanning rays (ISF).

Fig. 21. Comparison of simulated and measured forces using the thermomechanical model [38].

tool position along the tool path [98,99]. The closed form solutions give the exact values of maximum states directly and accurately thus avoiding the time marching solutions. They argued that a time marching simulation can still be conducted if the process needs to be interrogated at an unsafe tool path position.

and analysis of the chip geometry if the cutter–part engagement conditions do not change along the path [117]. Alternatively, the delayed differential equations can be solved at discrete time intervals using the semi-discretization method [14], which leads to a prediction of chatter stability, cutting forces, dimensional surface errors and vibrations along the tool path [59]. The process damping reduces the vibration amplitudes at lower speeds and increases the stability demonstrated by Budak et al. [41,42]. Although the depth of cut is the same, the vibration amplitudes are reduced significantly at the lower cutting speed due to the process damping effect, as in Fig. 23. The process damping, which affects the prediction of surface quality at lower speeds [124,125], can be easily considered in time-domain simulation models, as shown in Eq. (11).

3.4. Time-domain simulation of machining processes As the tool travels along the tool path, the cutter–part engagement boundaries are evaluated at each discrete path segment by one of the methods presented in Section 2. The cutter–workpiece engagement maps contain the axial depth of cut and width of cut along the cutting edge engaged with the material. There are various approaches in simulating the process states such as forces and vibrations. The time marching methods revolve the spindle and move the tool along the tool path at discrete time intervals and simulate the process states as a function of time [114]. The process equations (Eq. (11)) are solved at each time interval as demonstrated in boring [22,23,88], five axis milling [51,89,109]. Such methods are computationally costly, and fine sampling intervals (i.e. 0.2 ms to capture 5000 Hz vibration marks) are needed in order to simulate the maximum values of forces in periodic processes like milling. However, there have been efforts to improve the computational speed of time domain simulations. Surmann et al. [118] developed a time-domain simulation system for the milling process, in which it is not necessary to explicitly solve the process equations. By modeling the tool and the workpiece using CSG (Section 2.1), an implicit model of the chip is obtained for each tooth feed. The process forces are then calculated by analyzing this chip shape using scanning rays for the determination of the undeformed chip thickness (Fig. 45b), and subsequently applying an empirical force model. In order to predict the tool vibrations, the process forces are applied to a system of single-degree-of-freedom oscillators, which is used to describe the dynamic behavior of the cutting tool [118]. This approach was extended and applied for the modeling of workpiece vibrations by Kersting et al. [86,87]. The resulting tool and workpiece deflections are added to a CSG model of the tool– workpiece engagement that generates the chip shape in the subsequent tooth period in order to consider the regenerative effect [135]. The numerical simulation thereby provides a prediction of cutting forces, relative vibrations between the tool and workpiece, and dimensional form errors left on the finish surface as shown in Fig. 22 [29,30]. The computational speed of this approach can be further increased by avoiding the remodeling

Fig. 23. Vibration amplitude under process damping effect [41].

3.5. Grinding operations Grinding processes can be simulated similarly to metal cutting by evaluating the engagement geometry between the grinding wheel and the workpiece along the tool path as presented in Section 2. An overview of the grinding process modeling and simulation techniques is given by To¨nshoff et al. [121], Brinksmeier et al. [34] and Brecher et al. [31]. Besides analytical methods, process simulation models based on geometric-kinematic approaches, finite element analysis and molecular dynamics are widely used. In contrast to the previously described metal cutting processes, the cutting edges are not specified in abrasive processes due to the large amount of grains and the variation of their sizes, shapes, distribution and orientations. Unlike in metal cutting where the cutting edges are defined and have a line contact with the material, the grinding wheel has a contact surface which can be quite complex in multi-axis machining where free-form surfaces are generated

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[122]. In addition to the workpiece material properties, wear and deflections of the wheel strongly influence the grinding process [78]. In analytical approaches, which can be used for the simulation of process forces and operation states such as the resulting maximum height of profile in surface grinding, the tool and workpiece material properties are described by empirically determined constants [121]. The specific normal force F 0n , per unit width of cut (ap), can be expressed for surface grinding processes (Fig. 24a) as [34]: F 0n ¼ cw p cgw

e1 vf e3 ae2 e deq ; vc

(16)

Fig. 25. Geometric-kinematic simulation of a flat surface grinding process. (a) Distribution of the grains on the grinding tool and the topography of the ground surface. (b) Dexel-based representation of the workpiece with a dexel distance of 3 mm (ISF).

values of grinding processes [24]. In order to calculate the process forces acting on the tool, two different methods can be applied [113]. In the first method the engaged chip cross sectional areas Acu have to be summed and multiplied by an empirically determined specific cutting force coefficient kc,sim [24] (Fig. 24): FðtÞ ¼ kc;sim

N X Acu;i ðtÞ:

(17)

i¼1

For the second force model, the undeformed chip thicknesses have to be determined within the simulation system [113]. The force model for each grain can be expressed as: F ¼ nkc;sim bd0

Fig. 24. Engagement situation of tool and workpiece in (a) longitudinal surface grinding (IFW) and (b) NC grinding (ISF).

where ae is the depth of cut, deq is the equivalent grinding wheel diameter which reflects the wheel engagement, and vc and vf are the cutting speed and the feed velocity, respectively. Additionally, the coefficients cwp for the workpiece material, cgw for the grinding wheel and e1, e2 and e3 for the process parameters have to be determined empirically from experiments. An extended version of these models has been applied to other grinding operations such as internal or external grinding, which require the contact area between the wheel and the workpiece [34]. However, the applicability of analytical models to grinding operations with varying contact conditions (e.g. profile grinding [55,88,132]) is limited. Geometric-kinematic simulation models offer a flexible solution for the determination of the tool engagement with the workpiece in such grinding operations (Fig. 24b)[132]. The basic shapes of the grinding tools can be represented with solid modeling techniques (e.g. CSG) as described in Section 2. The contact area or the material removal rate can be calculated by intersecting the tool and the workpiece models [34,112]. Similar to the analytical approaches, process forces can be calculated using empirical methods, and the influence of the distribution and shapes of the grains are considered by the calibration of empirical constants. Instead of an implicit representation of the cutting grains, the geometry of the individual grains on the grinding tool can be modeled [28,113]. These grains can be generated based on statistical distributions [34,113] or measured tool topographies [78]. The extended model can be used to estimate the topography of the workpiece after finishing (Fig. 25a) or to determine characteristic



0 1mc;sim

d d0

(18)

where n is the normal direction of the cutting grain face, b the width of a considered segment of the cutting edge and d the undeformed chip thicknesses. The coefficients kc,sim and mc,sim have to be separately determined empirically for the calculation of normal and tangential forces. For example, by applying the simulation to drill-grinding processes, the material removal per individual grain was analyzed in order to allow improvement of the tool layout and thereby to avoid material clogging [28]. The same basic approach was utilized for simulating the NC grinding of free-formed surfaces [113]. By analyzing the chip shape for each cutting grain, it is possible to calculate the process forces, which can be used to adapt the tool path for keeping the grinding forces at a desired level. For the definition and calibration of the process force model, the geometric-kinematic approach was coupled with a finite element simulation [112]. However, these extended simulation models are computationally costly due to small simulation time steps which are necessary to evaluate the changing process conditions during the rotation of the grinding tool. Moreover, high resolutions of the workpiece models are necessary due to the small contact area of the individual grains (Fig. 26).

Fig. 26. Simulated single grain engagement (ISF).

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4. Machine dynamic models The dynamics of the machine tool are modeled by the rigid body kinematics, CNC system and structural dynamics of the multi-axis machine system. The NC tool path is followed by the machine at a feed velocity governed by the kinematics and control of the machine used by the CNC system. Any change in the feed along the tool path affects the chip load, and hence the cutting process. 4.1. CNC and trajectory generation models of machines A detailed review of CNC systems, their architecture and functional modules has been presented in previous CIRP keynote papers [15,107]. The virtual design and simulation of CNC systems [66], which include trajectory generation [63], drive dynamics [64] and servo control [65] have also been presented in detail by Altintas’s research group [139,140]. Here, only the kinematics of multi-axis machines and trajectory generation are briefly summarized since they are most relevant to the prediction of chip thickness, forces and vibrations in the virtual machining of a part. NC programs contain the tangential feed velocity, tool tip and orientations on the part coordinate (P) system as shown in Fig. 27. The tool motions are transformed into axes commands in the machine tool coordinate system (M) by the inverse kinematics of the machine tool. The tangential feed commanded by the NC program has to go through acceleration, constant feed and deceleration stages without saturating the torque limits of each drive, while preserving jerk continuity in order to have the smooth velocity profile as shown in Fig. 28 [4]. The feed varies during the acceleration and deceleration stages, and may not even reach the programmed values in the 3- to 5-axis machining of sculptured surfaces if the path segments are short and the drives do not have high accelerations (i.e. torque) [111]. For example, if the jerk is constant (J0), the acceleration will linearly increase, stay constant, and linearly decrease when the feed reaches a desired, cruising velocity. The machine will start decelerating by following the reverse phases of acceleration. As a result, the feed will have seven distinct variation zones within one NC block in the part program as follows [4]: 8 > J t2 > > fs þ 0 > > 2 > > > > f 1 þ At > > > > J0 t2 > > > < f 2 þ At  2 f ðtÞ ¼ f3 ¼ f4 > > > J t2 > > f4  0 > > 2 > > > f 5  At > > > > > J t2 > : f 6  At þ 0 2

zone h1i h2i

Fig. 28. Trajectory generation profile with jerk continuity [63].

Correct feed must be calculated by considering the trajectory profile of the CNC so that the chip loads, and hence the cutting loads, are estimated accordingly. If the jerk is a second order continuous system, the feed changes in nine distinct zones during the machining of one NC block, which has to be considered in predicting the process in virtual environments [4]. Including the effect of the servo dynamics will not drastically improve the evaluation of feedrates, i.e. chip loads, and hence it can be disregarded for virtual machining process simulations. 4.2. Stability inspection of machining processes

h3i h4i h5i h6i h7i

(19)

Process planners must select chatter-free global cutting speeds and cutting conditions in preparing NC programs. However, since the tool-part engagement conditions vary continuously along the tool path, the virtual machining system must alert the planner to the possibility of having chatter at specific locations. Instead of predicting the chatter-free stability lobes, virtual machining must inspect the presence of chatter and change the spindle speed, if possible, or alert the planner to modify the process. The transfer function of the structural dynamics of the machine measured at the tool–workpiece contact zone can be constructed in Laplace domain in the machine coordinates as: 2 3 Fxx ðsÞ Fxy ðsÞ Fxz ðsÞ 4 F ¼ Ft þ Fw p ¼ (20) Fyy ðsÞ Fyz ðsÞ 5: sym: Fzz ðsÞ The corresponding relative vibrations between the tool and workpiece can be predicted by applying the total cutting force (Eq. (11)) on the machine at the tool–workpiece interface: q31 ¼ F33 F31

(21)

where q31 represents the relative vibrations between the tool and workpiece: q ¼ qt þ qw p

Fig. 27. Kinematics of five-axis machine tool [141].

(22)

The vibrations (Eq. (21)) and total dynamic cutting forces (Eq. (11)) cannot be directly solved in time domain due to the

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regenerative term ðDqðtÞ ¼ qðtÞ  qðt  TÞÞ with the time delay (t  T) in the dynamic cutting force (Eq. (12)). The process can either be solved in time domain using numerical integration methods [100], or by converting the delayed dynamic equation into an analytical, semi-discrete form [14,80]. However, it is more practical to check whether the cutting system is stable or unstable at the particular tool path position in the frequency domain as follows. The critical stability of the system can be determined by setting the displacements and forces as: qðtÞ ¼ qð ¯ vc Þeivc t ;

¯ vc Þeivc t FðtÞ ¼ Fð

(23)

where vc is the chatter frequency and q¯ and F¯ are the amplitudes of displacement and force vectors, respectively. Similarly, the delayed displacement vector can be expressed as: qðt  TÞ ¼ qe ¯ ivc t eivc T :

(24)

Some of the machining operations, such as milling and boring with an uneven number of inserts on the cutter, have time periodic coefficients in their dynamic cutting forces (Eq. (11)). By averaging the periodic coefficients in tooth passing or spindle periods [6], the dynamic cutting force with regenerative and process damping parts that affect the chatter stability [9,138] can be described as: FðtÞ ffi ½ðA0 ð1  expðivc TÞÞ þ C0 Þdzqe ¯ ivc t

(25)

where the averaged directional matrix (A0) is given as [35,36] A0 ¼ V

Z

1=V 0

N X j¼1

Z 2p X N 1 f ðtÞdt ¼ f ðcÞdc: 2p 0 j¼1

(26)

11

CAM systems [40,45,46]. However, since Eq. (30) is speed dependent due to process damping, and the depth of cut is already assigned, the stability is solved using the Nyquist criterion [68,69]. For a specific spindle speed, the critical stable depth of cut is identified by increasing the depth of cut ða ¼ K  dzÞ until the cutting system becomes critically stable. If the depth of cut identified from the cutter–part engagement module of the virtual machining system is greater than the predicted critical depth, a chatter alert is sent to the process planner at the analyzed tool path location [11]. The structural dynamics of the system may vary along the tool path either due to the kinematic configuration of the machine tool, structural changes in the part or removal of the mass from the thin walled parts. It is possible to attach specific FRFs at each tool location along the NC tool path in virtual environments [12,30], and to check the corresponding chatter stability. However, if the dynamics change rapidly, the part machining needs to be simulated in time domain, and the FRF of the structure must be updated as the chips are removed from the part. Budak et al. [52] proposed an FRF updating scheme based on a matrix inversion method [102]. The FRF of the final part shape after machining is predicted with the FE method and used as a core model. The mass [M], structural damping [H] and stiffness [K] matrices are identified a priori. Instead of removing machined elements from the part, un-machined elements with their incremental mass [DM], damping [DH], and stiffness [DK] are added at each tool path location and the FRF is updated as:  1 ½g  ¼ ½½K þ ½DK  v2 ½½M þ ½DM þ i½½H þ ½DH

(31)

where [g] is the receptance matrix and v is the vibration frequency in rad/s. The method was applied in virtual, five-axis ball-end milling of turbine blades as shown Fig. 29.

Since the cutter is engaged only when it is between entry (fst) and exit (fex) angles (fst < c < fex), Eq. (26) can be simplified as: 8 9 2 3 f Zex X < K rc ðkdzÞ = e ðt; kdzÞ N X K 1 j 4 5  dc T 01 K tc ðkdzÞ A0 ¼ : ; sinðkr ðkdzÞÞ 2p j¼1 k¼1 K ðkdzÞ ac 2fst 3 8 9 f Zex < K rc ðkdzÞ = e ðc; kdzÞ K X N N 1 j 6 7 A dc5 ¼ ¼ 4 T 01 K tc ðkdzÞ : ; sinðkr ðkdzÞÞ 2p k¼1 2p 2 K ac ðkdzÞ f

(27)

st

Similarly, the process damping matrix (C0) in Eq. (25) is evaluated by averaging as [59]:  Z fex X N X K  e j ðt; kdzÞ 1 K s p L2w T01 P dc 2p fst j¼1 k¼1 4vc sinðkr ðkdzÞÞ "Z # K fex K L2 e j ðc; kdzÞ N X N K s p L2w sp w dc ¼ ¼ T01 P C  2p k¼1 fst 2p 4vc sinðkr ðkdzÞÞ 4vc C0 ¼

(28) Fig. 29. FE-based structural modification. (a) FE mesh of workpiece and stock, (b) element determination [52].

The generalized A0 and C0 matrices become constant and time invariant, but dependent only on the geometry of the tool and the kinematics (A, C) of each operation as in the case of general force models. Although considering time varying, periodic coefficients in multi-frequency [35,96] or semi-discrete [14,80] solutions leads to more accurate stability solutions when the radial depth of cuts are small, they are computationally more costly to use in virtual machining applications. By substituting Eq. (21) into Eq. (25), the stability of machining operations is reduced to the following, generalized eigenvalue problem: h i o n ¯ ivc t ffi 0 I  ð1  eivc T ÞA0 þ C0 Fðvc Þdz Fe (29) which leads to the following characteristic equation:

i o n h det I  1  eivc T A0 þ C Fðvc Þdz ¼ 0:

It is common to use multi-functional machines such as mill-turns in industry. While the simulation of the process forces, power and torque of such operations can be simply done by superposing process states, their chatter stability inspection differs due to the coupling between them. A parallel turning system shown in Fig. 30 has the following coupled dynamics [39,49]:

(30) Fig. 30. Geometry of simultaneous turning and milling [49].

The chatter-free critical depth of cut and spindle speeds can be directly predicted in frequency domain for milling [3,6], turning [103] and drilling [110,120]; the form errors can be predicted and constrained for process planning during NC tool path generation in



z1 ðtÞ z2 ðtÞ



 ¼

 z 1 iv c t e ; z2



F 1 ðtÞ F 2 ðtÞ



 ¼

 F 1 iv c t e F2

(32)

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The stability analysis leads to optimal metal removal conditions without violating the chatter limits of both tools used in the operation, see Fig. 31. Since multi-functional machines are more widely used, the chatter stability of combined turning, milling, drilling and grinding operations must be further developed to detect the abnormalities along the tool path in the virtual machining environment.

Fig. 31. Stability diagram for a parallel turning operation with two tools. Experimental results are shown by markers [39].

5. Virtual optimization of machining operations

Fig. 33. Flow chart of the optimization procedure [UBC MAL].

Machining parameters such as speeds, feedrates and depths of cut need to be selected properly in order to improve the process efficiency and quality of the finished product. An optimization strategy is proposed to determine the most efficient machining parameters based on the process physics [99]. The machining constraints include the cutting forces, chip thickness, spindle torque-power, form errors on the workpiece and system stability. The objective of the optimization is to maximize the Material Removal Rate (MRR), which is defined as [97]: MRR ¼ a B c n N

(33)

where a is the axial depth of cut, B is the radial width of cut, c is the feed per tooth, n is the spindle speed, and N is the number of flutes on the milling cutter, as depicted in Fig. 32.

Fig. 32. Milling process with design variables [99].

The optimization strategy is divided into a pre-process and post-process optimization as illustrated in Fig. 33.

stability. Therefore, the objective function of the pre-process optimization reduces to [99]: f ob j ¼ a B c:

(34)

5.1.1. Chatter stability constraint Selection of the depth of cut, width of cut and spindle speed should lead to stable cutting conditions, which is determined by the stability lobe. Fig. 34(a) shows the stability lobe with a constant width of cut (B), and Fig. 34(b) shows the stability lobe with a constant depth of cut (a) when the width of cut is normalized with respect to the tool diameter. By locating the optimum spindle speed from these two figures, the design space shown as Fig. 34(c) is formed by extracting a maximum stable depth and width of cut at a constant spindle speed [98]. Stability limits increase drastically with the effect of process damping at low cutting speeds [8]. Although its accurate mathematical modeling is still an ongoing research topic, the cutting edge geometry, flank wear, work material’s contact resistance, vibration frequency and cutting speed highly affect the stability with process damping [138] as shown in Fig. 35 [124]. The inclusion of process damping in selecting a chatter-free, optimal depth of cut is therefore crucial in machining thermal resistant alloys in the aerospace industry. 5.1.2. Machine tool torque/power constraint In order to avoid spindle stall, the torque/power limit of the machine tool should not be exceeded. Both torque and power are dependent on tangential cutting force Ft (w) as [97]: 8 > > <

5.1. Pre-process optimization

D Torque ðfÞ ¼ F t ð’Þ Nm 2

 Dpn > > 1:34102  103 hp : Power ð’Þ ¼ F t ð’Þ 60

Pre-process optimization provides an upper bound of the cutting parameters to obtain an efficient machining operation during the process planning stage. Since the cutter is chosen before selecting the cutting conditions and generating the tool path, the number of teeth of the tool (N) is known prior to the optimization. The spindle speed (n) is selected based on tool life and chatter

where D is in (m), n is in (rev/min), and Ft is in (N). An analytical solution of the tangential force for a cylindrical end mill is derived as a function of an angular position w [99]. The critical angular positions ’tmin and ’tmax corresponding to global minimum and maximum tangential forces are obtained. In order to prevent the violation of torque/power constraints, the

(35)

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cumulative average torque and power, which are defined as:

qcav

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi v" #ffi u u qð’t Þ2 þ qð’t Þ2 max min t ¼ : 2 q ¼ fTorque;

(36)

Powerg

must not exceed the machine tool torque/power limitations of the machine tool at spindle speed n: Torquecav T mo ðnÞ;

Powercav Pmo ðnÞ

(37)

where Tmo and Pmo are machine tool torque and power curves provided by a manufacturer as shown in Fig. 36.

Fig. 36. Torque-power characteristics of a machine tool [99].

5.1.3. Chip thinning constraint When the width of cut is smaller than the radius of the tool (i.e. b < 0.5), the maximum chip thickness does not reach the commanded feed per tooth. A very small chip thickness leads to a low material removal rate and to ploughing of the cutting edge on the workpiece instead of cutting. The chip limit must be maintained by manipulating the feed rate as the width of cut varies. The feedrate cmax, which generates the desired maximum chip load hmax, is calculated as:

cmax

8 9 hmax < pffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi i f 0 < b 0:5 = : ¼ 2 bð1  bÞ : ; hmax otherwise

(38)

The constraints are identified ahead of optimizing the NC programs as the tool traverses along the tool path. 5.2. Post-process optimization

Fig. 34. Chatter stability lobes for constant axial depth of cut, constant radial depth of cut and constant speed [99].

Once the NC program is generated, the width and depth of cut, i.e., the NC tool path, are defined. However, since the part geometry varies along the tool path, the desired depth of cut and width of cut cannot be maintained, which leads to a variation in the cutting process. The cutter–part engagement boundaries along the tool path are identified at desired displacements along the tool path, and the feed and speed are optimized by respecting the physical constraints of the process as outlined in the previous section. The constraints include the maximum chip load, the resultant force on the tool, form errors left on the part, and the torque/power characteristics of the machine tool. 5.2.1. Feedrate optimization The majority of the process output is linearly dependent on the feedrate in the following form, provided that the cutting force coefficients are independent of the feedrate:

Fig. 35. Effect of cutting parameters on process damping and stability in turning (MRL).

F q ð’Þ ¼ Aq0 ð’Þ þ Aq1 ð’Þc ðq x; y; z; t; r; trq; pwrÞ

(39)

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where subscripts x, y, z, t, r are the cutting forces in the feed, perpendicular to feed, axial, tangential, and radial directions; and trq, pwr are the spindle torque and power, respectively. By numerically solving the angular position ’qmax at which the process output becomes the maximum defined as Fq,max, the maximum allowable feedrate is solved as: q cmax ¼

F q;max  Aq0 ð’qmax Þ : Aq1 ð’qmax Þ

(40)

The maximum resultant force in the xy plane is a quadratic function of the federate [99], shown as: qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi res res 2 F res ð’Þ ¼ Ares (41) 0 ð’Þ þ A1 ð’Þc þ A2 ð’Þc : The maximum feedrate is obtained by solving the root of Eq. (41) when the resultant force is equal to the user-defined maximum Fres,max. Considering the form errors in milling, the maximum allowable d fl feedrate for a user-defined deflection constraint dmax is solved as: d fl ¼ c1 þ ðc2  c1 Þ cmax

dmax  d1 d2  d1

(42)

where c1, c2 are two arbitrary feed rates, and d1, d2 are the corresponding calculated deflections.

rates due to the continuously varying workpiece geometry, and result in the saturation of axis motors and undesired feed marks on the finished surface; therefore, optimized feed rates are filtered by considering the trajectory generation parameters of the CNC system as described in the machine dynamics section. It is particularly important to achieve smooth feed changes to avoid marks on the finish surface without violating the jerk and acceleration limits of the drives [67,111]. 5.2.4. Tool path optimization Tool path selection is one of the critical parameters in planning machining processes. In the commercial CAM packages, tool paths are selected from standard path libraries by considering only the geometry of the part. New approaches are needed to generate optimized tool-paths for complex free-form surfaces. In recent studies [90,94], the physical relationship between the mean resultant forces, cycle times and scallop heights were introduced. Since the three critical process outputs conflict with each other, the optimal path strategy was identified by using the objective weighting algorithm. Moreover, the method allows for observation of the trade-off between each criterion, and determination of the corresponding toolpath for each solution. A sample threedimensional free-form surface generated with the optimized tool path strategy is shown in Fig. 38.

5.2.2. Feedrate and spindle speed optimization The optimization of spindle speed is based on the constraints of the torque-power characteristics of the machine and the stability limit. For a fixed depth of cut, the feasible spindle speed is in the range that results in a stable cutting condition as:   n nmax;i ; ðn nmin;i Þ (43) where nmin,i and nmax,i are the lower and upper spindle speeds under a critically stable condition for the ith lobe. The graphical representation of the design space considering the nonlinear torque and power constrains is shown in Fig. 37. The spindle speed and feedrate corresponding to the optimum solution (maximum MRR) are obtained at the limits of the constraints, which is marked with a star in the Fig. 37.

Fig. 38. Simulated and machined free-form surface and optimized tool path in 3D.

The cycle times can be considerably long in the finish machining of highly flexible gas turbine blades [47]. The structural dynamics of the blade varies both, in the axial and along the tool path as shown in Fig. 39. The blade is far more flexible than the tool, and it becomes more rigid as the tool moves from the tip to the

Fig. 37. Graphical representation of design space [98].

5.2.3. CL file updating The original cutter location (CL) file is used to obtain the cutter– workpiece engagement along the tool path, then the feedrate and spindle speed are adjusted based on the user-defined constraints. The optimization process might generate highly fluctuating feed

Fig. 39. Stability of finishing and multi-level finishing.

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hub of the blade. As a result, the stability limits and natural frequencies vary along the blade surface as the cutting tool removes the material [52]. A multi-level cutting strategy for blade finishing is found to be more feasible in a virtual machining environment, as opposed to the commonly used constant cutting depth strategy (see Fig. 39). The blade surface is divided into four segments along the axial direction. For each segment the spindle speed and cutting depth are selected according to the largest stability pocket of the corresponding segment. By changing the cutting depth in the multi-level finishing strategy, the cycle time is decreased from 35 minutes to 19 minutes while still respecting the part quality requirements.

6. Virtual part machining examples Typical virtual machining software is shown in Fig. 40. The system reads the standard CL file from a CAM system, parses the NC program and separates the operations by identifying tool change commands, and either simulates or optimizes the process by predicting/optimizing the maximum forces, torque, power, form errors and chip loads along the tool path. The system automatically breaks large tool paths into smaller segments by respecting the CNC’s trajectory generation profile and gives a new CL file with optimized feed commands. The original NC tool path geometry is unchanged. There are few commercially available virtual machining systems, and they all operate in a similar fashion except that their process models and optimization strategies may differ. The prediction accuracy of virtual machining systems is most affected by the cutting force coefficients, the mathematical model of the physical processes and cutter–workpiece engagement conditions [50]. Any error in these three modules will reflect directly on the accuracy of the virtual prediction and optimization of NC programs. The cutting force coefficients can be mechanistically calibrated for each cutter–work material couple to minimize their effect on the prediction accuracy for the mass production of parts, but generalized orthogonal-to-oblique cutting transformation methods would be less costly for small batch production [2]. It is ideal to digitize the cutter–part engagement conditions at feed rate increments so that any small change in part geometry can be detected along the tool path. However, a densely digitized tool path may unnecessarily increase the computation time and make the virtual machining system unfeasible to use in production. Fig. 41. Virtual machining of a stamping die, predicted maximum cutting forces with MACHPROTM and measured forces along the tool path (Courtesy of Sandvik Coromant and MAL Inc.).

Fig. 40. A sample view of a virtual machining system. (Courtesy of Sandvik Coromant and MAL Inc.).

6.1. Metal cutting applications A sample three-axis milling of a stamping die with circular inserts is shown in Fig. 41. The accuracy of the simulation system was verified by comparing the predicted and measured maximum cutting forces along the tool path. The process had an experience of

severe chip thinning at sharp curvatures, which was eliminated while decreasing the machining time by 25% with the feed regulation. Virtual machining is most crucial in manufacturing very costly aerospace parts, since their trial- and error-based optimization may be cost prohibitive. A sample optimization of a jet engine impeller is shown before and after virtual machining in Fig. 42. The cutter was a taper helical ball end mill, whose pitch angles are designed to maximize the chatter-free depth of cuts at the desired cutting speeds [18,43,44]. The virtual process optimization parameters included maximum stress at the tool shank to avoid tool failure, chip load and torque limit on the spindle and feed drives [47,70,71]. Virtual machining with process forces and deflections can be complemented with visualization by updating the part geometry using solid modeling methods. The result of simulating the peripheral milling of a turbine blade with modeled workpiece and tool vibrations is shown in Fig. 43a. Both spherical end mill and workpiece geometries were modeled by CSG where the workpiece and tool deflections were applied by altering the CSG model of the tool for each tooth feed. This allows the visualization of the vibration marks on the finish surface by rendering with ray-tracing

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Fig. 42. Five axis flank milling of a jet engine impeller before and after optimizing with virtual machining system. The cycle time is reduced by 62% and the surface finish is improved by 8.4 fold. (Courtesy of Pratt & Whitney Canada).

Fig. 44. Toolpath and the broken tool (MRL).

indicated that the axial cutting force increased from 150 N to 450 N as seen in Fig. 44, where the tool was digging into the material due to a negative effective lead angle [58]. The problem was solved by re-orienting the tool axis vector that minimized the indentation of the tool into the material [126,127]. The effect of tool orientation, i.e. lead and tilt angles, on the process mechanics is quite important

Fig. 43. Simulated surface structures. (a) Simulation of the machining of a turbine blade taking workpiece vibrations into account [29]. (b) Simulation surface location errors, (c) simulation of structures generated during face milling [32].

techniques (Fig. 43b) [29,118]. For the modeling of the surfaces generated during face milling, another modeling technique has to be applied. Since the tool is constantly engaged with the workpiece, an altered CSG model for each tooth feed is not sufficient. For the simulation of this process, the geometry of the cutting edge can be approximated by an open traverse line, whose points are adjusted corresponding to the tool deflections. By connecting the lines from subsequent time steps, a triangulated sweep surface is obtained, which can be projected directly onto a height field in order to visualize the surface structure (Fig. 43c). Another approach allows the direct modeling and visualization of the surface location error without the need for ray-tracing or triangulation techniques. It is based on the combination of the CSG technique for the determination of the process forces and the dynamic behavior, and a dexel-based workpiece model for the visualization of surface location errors. Each time a dexel is cut, the intersection points can be displaced by the projected vibration amplitude. This applied displacement also corresponds directly to the surface location error (Fig. 43b). The combination of the two different workpiece models also permits the identification of constant engagement situations for the optimization technique described in Section 3.4. Alternatively, it is possible to estimate the errors left on the finish surface by correlating the measured forces and stiffness of the system in a virtual environment as well [54]. A problematic tool path for roughing of an integrally bladed rotor, which leads to frequent breakage of a serrated tool at its ball end, is shown in Fig. 44. The multi-axis virtual machining software

Fig. 45. Roughing toolpath and kinematic profiles for a sample pass, resultant cutting forces and cycle time comparison [67].

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as presented by Ozturk et al. [106] who reported that the lead angle of about +10 degrees would be most suitable as demonstrated in this application. Once the optimal feed is identified from the process, a further reduction in machining time can be achieved by optimizing the trajectory profiles in three- to five-axis machining of free-form surfaces. The CNC systems may not be able to achieve the commanded feeds when the paths have sharp curvatures, demanding high torque and acceleration from feed drives. It is possible to reduce the machining cycle time by re-shaping the trajectory commands along the curved paths without violating the feed drive limits and process-imposed federate along the tool path. Erkorkmaz et al. [67] and Sencer et al. [111] demonstrated that up to a 65% reduction can be achieved in the machining of free-form parts via trajectory optimization. The tool path for a free-form surface shown in Fig. 45 was generated with 0.04 mm tolerance, leading to 11,186 CL points for the complete operation. Processing by the solid modeler, applying the B-rep method, took 482 s and consumed 1.2 GB of memory. In feed planning, the resultant force limit was set to 250 N. A Mori Seiki NMV 5000DCG machining center was used in the cutting tests. The drives’ velocity, acceleration and jerk limits were identified by inspecting the corresponding registers in the CNC. The process was first optimized by varying the feed while keeping the cutting force at 250 N [62]. At the second step, the feed was optimized again by respecting the acceleration and jerk limits of the drives, which led to a further 17% reduction in machining time [67]. 6.2. Grinding applications

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processes requires detailed knowledge about the distributions of these process parameters. A virtual environment is developed to simulate and optimize five-axis grinding with complex wheel and work geometries [75]. Boolean intersection algorithms are used to determine the shape and geometrical characteristics of the wheel– workpiece contacts at any instant. The contact geometry is then analyzed as a stack of discrete sub-wheels for which the physical process parameters such as forces, power and temperature are predicted using the contact data. Grinding examples show that the grinding process parameters vary significantly along the wheel axis at any instant and along the grinding path. The grinding process is far from optimal if a constant workspeed is used. Multiconstraint optimization is then applied to optimize the workspeed to reduce cycle time while maintaining grinding forces, power and temperature below the specified limits as shown in Fig. 46. The optimization leads to about a 40% cycle time reduction, while the maximum power is reduced by >30% and the maximum force reduced by about 35% [75]. A good example of a virtual grinding application is the manufacturing of tungsten carbide end mills. Due to the hardness of tungsten carbide, cutting tools are manufactured using deep grinding processes with diamond or CBN-grains. Because of the large grinding forces, the flexible end mill which has varying stiffness along its axis, experiences static deflections and vibrations. The deflections lead to geometrical errors and poor cutting edge grinding quality which need to be optimized through virtual simulations. The virtual model considers the kinematics of the 8axis tool grinding machine, the discrete removal of material and the tool-grinding wheel engagement zone, which are used to predict the grinding process [53] (Fig. 47).

When grinding complex parts such as airfoils and blade retention slots as shown in Fig. 46, the complex wheel/workpiece geometry and multi-axis motion result in variable wheel– workpiece contact. All grinding process parameters such as depth of cut, wheelspeed, workspeed, forces and temperature will vary along the wheel axis and grinding path. Optimizing such grinding

Fig. 47. Calculated material removal rate Qwa(i) (mm3/s) and (b) calculated equivalent chip thickness heq (mm) for a tool grinding process (vft = 30 mm/min, d = 10 mm, R = 62.0 mm) [53].

Fig. 46. Grinding of jet engine impellers with tapered ball ended tools (a) and form grinding (b) of turbine blade retention slots. (Courtesy of United Technologies Research Center).

The workpiece discretization is based on dexel grids in all three coordinate directions. The cylindrical grinding wheel is approximated by a non-rotating polyhedron which is reduced to a pie segment. During each simulation step, the grinding wheel moves in a step-wise linear motion, and the intersection of the sweep volume and workpiece is calculated to estimate grinding forces. To map values to different areas, the grinding tool is divided into slices along the tool axis. By dividing the removed volume of each dexel

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at discrete simulation time steps and mapping it to the corresponding slice, the distribution of the material removal rate Qwa (Fig. 47a) and equivalent chip thickness heq (Fig. 47b) can be determined for each element. A constant cutting speed is assumed, which is reasonable due to the cylindrical shape of the grinding wheel. To parameterize an empirical grinding force model, flat grinding experiments using a cylindrical grinding wheel and a rectangular workpiece have been carried out on a Walter Helitronic Power tool grinding machine. The resulting forces for different depths of cut have to be related to geometric parameters, which can both be evaluated in the 3D dexel simulation. The factor heq/dlg relates the equivalent chip thickness heq to the local contact length dlg for each segment of the contact area. This dimensionless value replaces the factors for feed speed, cutting speed and grinding wheel diameter, which are commonly used in grinding force models [121]. The grinding forces are applied on the position-dependent structural model of the ground end mill at each simulation step. Since a deflection directly affects the contact conditions and therefore the acting grinding forces, the coupled process–structure interaction is handled with an iterative model. The algorithm determines the deformation of the workpiece and machine structure during grinding at a static equilibrium of the grinding force, and the spring-back force of the workpiece until the deformations calculated in the previous and current steps converge within a defined tolerance. Subsequently, the material is removed, the current shape is defined and the calculation step is repeated. Once the deflections are predicted by the virtual grinding model, the original tool path is modified to compensate them. The iterative simulation process is repeated until the predicted workpiece geometry and the ground end mill match with the desired geometry within its specified tolerance as shown in Fig. 48. The deflections of the ground end mill are reduced from 163 mm to 35 mm as shown in [53,55].

Fig. 48. Comparison of resulting workpiece cross-sections with uncompensated NCprogram (left) and compensated tool path (right) [53].

For optimizing the grinding of wear-resistant coated freeformed surfaces on machining centers using abrasive mounted points, a simulation system based on multi-dexel boards and CSGtechniques is used [113]. Due to the inhomogeneus thickness of the thermally sprayed coating and the varying contact areas between the grinding tool and the free-formed workpiece surface (Fig. 49), the grinding forces vary along the NC path. These variations can

Fig. 49. Process simulation of grinding of wear-resistant coated free-formed surfaces on machining centers using abrasive mounted points (ISF).

lead to shape and dimensional errors as well as surface defects. In order to counteract these effects, the NC path can be adjusted based on the simulation results. The process force model uses the engagements of the individual grains, as described in Section 3.5. In addition to the calculated forces, the generated surface roughness can be approximated within the simulation system. 7. Uncertainties in virtual machining Virtual machining is based on the mathematical modeling of part-tool engagement geometry, machining process physics, structural and rigid body kinematic motion of the machine and work material properties. The accuracy of the prediction will be dependent not only on how well the mathematical models approximate the complex machining process physics, but also on how accurately the input parameters are entered to the virtual machining system. The accuracy of mathematical models have been well reported in previous dedicated metal cutting [2,20], grinding [79,121], chatter stability [16] and virtual machine tool [5] key note articles. The effects of uncertainties in the key input parameters are summarized as follows. 7.1.1. Geometric variations Assuming that the part is accurately placed on the fixture and machine, the key source of uncertainties originate from casting, forging and semi-finish tolerances. The unexpected extra stock left on the part results in an under-prediction with the same proportion, i.e. 5% extra stock leads to 5% less magnitudes in predicted force, power and torque. The mesh size in the cutter– workpiece engagement affects the prediction results similar to the forging errors. 7.1.2. Cutting process models The key uncertainties originate from the cutting force coefficients, which depend on the tool geometry (rake angle, helix angle, nose radius, cutting edge radius and inclination angle), and material properties such as temperature-dependent hardness and flow stress, tool wear and lubrication (i.e. Coulomb friction). Errors in the cutting force coefficient affect the cutting forces, torque and power in the same proportion. Changes in the material’s yield shear stress directly and linearly affect the cutting force coefficient. For example, if the tangential cutting force coefficient for AISI steel is 1500 MPa with 10% uncertainty, the prediction of cutting forces, torque and power will also have the same uncertainty (10%). The effect of uncertainties in tool geometry varies. While the effect of rake and helix angles is about 2% per degree, the cutting edge radius and grain size of the grinding wheel have a major effect, especially in finish machining where the chip loads can have the same magnitude. It is recommended that the cutting force coefficients are calibrated against the edge radius, grain size and lubricant for each material as explained in [121,139]. 7.1.3. Machine dynamic models The forced and chatter vibrations between the tool center point and workpiece depend on the relative dynamic stiffness between the two and the process forces. The chatter stability is linearly proportional to the dynamic stiffness (2k ). If the measurement of the dynamic stiffness has an error of +10%, the chatter-free depth of cut will be underestimated by 10%. If the natural frequencies of the machine tool structure varies along the tool path due to kinematic and cross coupling effects [33], the stability pockets will also shift. One Hz error in the natural frequency measurement will result in a chatter-free spindle speed shift of 60 rev/min/N where N is the number of teeth on the tool. Often, the structural dynamics of thin-walled parts change significantly during machining, which need to be carefully mapped to the tool path as discussed in the dynamic section.

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7.1.4. CNC models

Acknowledgement

The positioning errors of CNC models have a negligibly small effect on process simulation in virtual machining. However, the chip loads in cutting and grinding are direct functions of feeds along the tool path. If the tool path is curved, especially in threeand five-axis machining of free-form surfaces, the feed changes continuously as a function of path curvature, acceleration and jerk settings of the CNC (i.e. trajectory generation). Although the trajectory generation settings of the CNC are considered in virtual machining [11,67,99], any uncertainty in the acceleration and jerk have a third and second order polynomial relationship with the feed, hence the forces as explained in the paper. Typically, 10–15% errors may originate from the approximated trajectory models of the CNC systems in virtual machining of free-form surfaces.

The following co-workers have assisted in preparing the manuscript: Murat Kilic, Dr. Xioliang Jin and Dr. Doruk Merdol (MAL, University of British Columbia), Dr. Volker Bo¨ß (IFW, Leibniz Universita¨t Hannover), Dr. Taner Tunc (MRL, Sabanci University), Dr. G. Guo (United Technologies Research Center), Mikael Lundblad (Sandvik Coromant Engineering Center).

8. Conclusion The modeling of machining operations has been evolving as an important engineering tool to simulate operation physics ahead of costly production trials of parts used in industry. Virtual machining technology has three important components which affect the accuracy of prediction, computational speed and visualization of the operation by the process planners. The first step is the evaluation of cutter–workpiece engagement conditions along the tool path. The tool and workpiece geometries are extracted from the CAM systems, and various solid modeling methods have been developed with varying computational efficiency, accuracy and visualization. The accuracy of virtual machining is directly related to the identification of the cutter– workpiece engagement conditions. Accurate engagement predictions lead to impractically long simulation times for industry, and rough engagement predictions lead to inaccuracies in process simulations. Further research is needed to improve both the accuracy and computational efficiency of cutter–workpiece engagement conditions. The mathematical models of metal cutting and grinding processes must be well developed with computational efficiency and accurate physics. There have been major advances in predicting the micro-mechanics of cutting (i.e. stress, temperature, white layer characteristics of the finish surface, thermo-mechanical behavior of the material) as well as the macro-mechanics of cutting (i.e. forces, torque, power, vibrations, part deformation errors). While the micro-mechanics models are used to design the process and to analyze the operation at specific part locations, the macro-mechanics are used to optimize the general machining operation in CAM environments. There are still challenges in achieving computationally efficient but accurate modeling of cutting forces, structural deformations and chatter stability when complex cutting tools are used in NC programs. The third component of the virtual machining system is to model the rigid body kinematic motion of the machine and its position-dependent structural dynamics. The tangential feed velocity between the workpiece and tool varies as a function of the machine’s kinematic and CNC configuration, which has to be included in predicting the chip loads along the tool path. The structural dynamics of both the machine and workpiece may also change along the tool path, and they need to be incorporated into the virtual machining system in order to predict deflections and vibration marks imprinted on the finish surface of the part. Furthermore, machine tools have volumetric errors which are reflected on the part. The volumetric errors of the machine tool must be integrated into virtual machining systems to predict these metrology errors. Although a significant amount of research still must be conducted to achieve highly efficient and accurate virtual machining systems, the current know-how is already useful in minimizing scrap rates and maximizing production efficiency in industry.

References [1] Abele E, Altintas Y, Brecher C (2010) Machine Tool Spinde Units. CIRP Annals 59(2):781–802. [2] Altintas Y (2000) Modeling Approaches and Software for Predicting the Performance of Milling Operations at MAL-UBC. International Journal of Machining Science and Technology 4(3):445–478. [3] Altintas Y (2001) Analytical Prediction of Three Dimensional Chatter Stability in Milling. Japan Society of Mechanical Engineers. International Journal Series C: Mechanical Systems Machine Elements and Manufacturing 44(3):717–723. [4] Altintas Y (2012) Manufacturing Automation, Cambridge University Press, UK. [5] Altintas Y, Brecher C, Weck M, Witt S (2005) Virtual Machine Tool. CIRP Annals 54(2):115–138. [6] Altintas Y, Budak E (1995) Analytical Prediction of Stability Lobes in Milling. CIRP Annals 44(1):357–362. [7] Altintas Y, Engin S (2001) Generalized Modeling of Mechanics and Dynamics of Milling Cutters. CIRP Annals 50(1):25–30. [8] Altintas Y, Eyniyan M, Onozuka H (2008) Identification of Dynamic Cutting Force Coefficients and Chatter Stability with Process Damping. CIRP Annals 57(1):371–374. [9] Altintas Y, Kilic ZM (2013) Generalized Dynamic Model of Metal Cutting Operations. CIRP Annals 62(1):47–50. [10] Altintas Y, Lee P (1996) A General Mechanics and Dynamics Model for Helical End Mills. CIRP Annals 45(1):59–64. [11] Altintas Y, Merdol DS (2007) Virtual High Performance Milling. CIRP Annals 55(1):81–84. [12] Altintas Y, Montgomery D, Budak E (1992) Dynamic Peripheral Plate Milling of Flexible Structures. Transactions of ASME Journal of Engineering for Industry 114:137–145. [13] Altintas Y, Spence A (1991) End Milling Force Algorithms for CAD Systems. CIRP Annals 40(1):31–34. [14] Altintas Y, Stepan G, Merdol D, Dombavari Z (2008) Chatter Stability of Milling in Frequency and Discrete Time Domain. CIRP Journal of Manufacturing Science and Technology 1:35–44. [15] Altintas Y, Verl A, Brecher C, Uriarte L, Pritschow G (2011) Machine Tool Feed Drives. CIRP Annals 60(2):779–796. [16] Altintas Y, Weck M (2004) Chatter Stability in Metal Cutting and Grinding. CIRP Annals 53(2):619–642. [17] Altintas Y, Xiaoliang J (2011) Mechanics of Micro-Milling with Round Edge Tools. CIRP Annals 60(1):77–80. [18] Altintas Y, Engin S, Budak E (1999) Analytical Prediction of Chatter Stability and Design for Variable Pitch Cutters. Transactions of ASME Manufacturing and Engineering and Science 121:173–178. [19] Ambati R, Pan X, Yuan H, Zhang X (2012) Application of Material Point Methods for Cutting Process Simulations. Computational Materials Science 57:102–110. ¨ zel T, Umbrello D, Davies M, Jawahir IS (2013) Recent Advances [20] Arrazola PJ, O in Modelling of Metal Machining Processes. CIRP Annals 62(2):695–718. [21] Armarego E (2000) The Unified-Generalized Mechanics of Cutting Approach – A Step Towards a House of Predictive Performance Models for Machining Operations. International Journal of Machining Science and Technology 4(3):319–362. [22] Atabey F, Lazoglu I, Altintas Y (2003) Mechanics of Boring Processes – Part I. International Journal of Machine Tools and Manufacture 43:463–476. [23] Atabey F, Lazoglu I, Altintas Y (2003) Mechanics of Boring Processes – Part II: Multi-Insert Boring Heads. International Journal of Machine Tools and Manufacture 43:477–484. [24] Aurich J, Kirsch B (2012) Kinematic Simulation of High-Performance Grinding for Analysis of Chip Parameters of Single Grains. CIRP Journal of Manufacturing Science and Technology 26(5):164–174. [25] Aurich JC, Biermann D, Blum H, Brecher C, Carstensen C, Denkena B, Klocke F, Kro¨ger M, Steinmann P, Weinert K (2009) Modelling and Simulation of Process: Machine Interaction in Grinding. Production Engineering Research and Development 3(1):111–120. [26] Benouamer M, Michelucci D (1997) Bridging the Gap Between CSG and Brep via a Triple Ray Representation. ACM Symposium on Solid Modeling and Applications 68–79. [27] Bergs T, Rodriquez CA, Altan T, Altintas Y (1996) Tool Path Optimization for Finish Milling of Die and Mold Surfaces – Software Development. Transactions of the NAMRI/SME XXIV 81–86. [28] Biermann D, Feldhoff M (2012) Abrasive Points for Drill Grinding of Carbon Fiber Reinforced Thermoset. CIRP Annals 61(1):299–302. [29] Biermann D, Kersting P, Surmann T (2010) A General Approach to Simulating Workpiece Vibrations During Five-Axis Milling of Turbine Blades. CIRP Annals 59(1):125–128. [30] Biermann D, Surmann T, Kersting P (2013) Oscillator-Based Approach for Modeling Process Dynamics in NC Milling with Position- and Time-Dependent Modal Parameters. Production Engineering Research and Development 7(4):417–422.

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G Model

CIRP-1256; No. of Pages 21 20

Y. Altintas et al. / CIRP Annals - Manufacturing Technology xxx (2014) xxx–xxx

[31] Brecher C, Esser M, Witt S (2009) Interaction of Manufacturing Process and Machine Tool. CIRP Annals 58(2):588–607. [32] Breitensprecher T, Hense R, Hauer F, Wartzack S, Biermann D, Willner K (2012) Acquisition of Heuristic Knowledge for the Prediction of the Frictional Behavior of Surface Structures Created by Self-Excited Tool Vibrations. Key Engineering Materials 504-506:963–968. [33] Bringmann B, Maglie P (2009) A method for Direct Evaluation of the Dynamic 3D Path Accuracy of NC Machine Tools. CIRP Annals 58(1):343–346. [34] Brinksmeier E, Aurich JC, Govekar E, Heinzel C, Hoffmeister HW, Klocke F, Peters J, Rentsch R, Stephenson DJ, Uhlmann E, Weinert K, Wittmann M (2006) Advances in Modeling and Simulation of Grinding Processes. CIRP Annals 55(2):667–696. [35] Budak E, Altintas Y (1998) Analytical Prediction of Chatter Stability in Milling-Part I: General Formulation. Transactions of ASME Journal of Dynamic Systems Measurement and Control 120:22–30. [36] Budak E, Altintas Y (1998) Analytical Prediction of Chatter Stability in Milling-Part II: Application of the General Formulation to Common Milling Systems. Transactions of ASME Journal of Dynamic Systems Measurement and Control 120:31–36. [37] Budak E, Ozlu E (2007) Analytical Modeling of Chatter Stability in Turning and Boring Operations: A Multi-Dimensional Approach. CIRP Annals 56(1):401– 404. [38] Budak E, Ozlu E (2008) Development of a Thermomechanical Cutting Process Model for Machining Process Simulations. CIRP Annals 57(1):97–100. [39] Budak E, Ozturk E (2011) Dynamics and Stability of Parallel Turning Operations. CIRP Annals 60(1):383–386. [40] Budak E, Tekeli A (2005) Maximizing Chatter Free Material Removal Rate in Milling through Optimal Selection of Axial and Radial Depth of Cut. CIRP Annals 54(1):353–356. [41] Budak E, Tunc LT (2009) A New Method for Identification and Modeling of Process Damping in Machining. Transactions of ASME Journal of Manufacturing Science and Technology 131(5). 051013 (10 pages). [42] Budak E, Tunc LT (2010) Identification and Modeling of Process Damping in Turning and Milling Using a New Approach. CIRP Annals 59(1):403–408. [43] Budak E (2003) An Analytical Design Method for Milling Cutters with Non Constant Pitch to Increase Stability-Part I: Theory. Transactions of ASME Journal of Manufacturing Science and Engineering 125:29–34. [44] Budak E (2003) An Analytical Design Method for Milling Cutters with Non Constant Pitch to Increase Stability-Part II: Application. Transactions of ASME Journal of Manufacturing Science and Engineering 125:35–38. [45] Budak E (2006) Analytical Models for High Performance Milling-Part I: Forces, Form Error and Tolerance Integrity. International Journal of Machine Tools and Manufacture 46:1478–1488. [46] Budak E (2006) Analytical Models for High Performance Milling-Part II: Process Dynamics and Stability. International Journal of Machine Tools and Manufacture 46:1489–1499. [47] Budak E (2000) Improvement of Productivity and Part Quality in Milling of Titanium Based Impellers by Chatter Suppression and Force Control. CIRP Annals 49(1):31–36. [48] Budak E, Altintas Y, Armarego EJA (1996) Prediction of Milling Force Coefficients From Orthogonal Cutting Data. Transactions of ASME Journal of Engineering for Industries 118(2):216–224. ¨ ztu¨rk E (2013) Stability and High Performance Machining [49] Budak E, C¸omak A, O Conditions in Simultaneous Milling. CIRP Annals 62(1):403–406. [50] Budak E, Lazoglu I, Guzel BU (2004) Improving Cycle Time in Sculptured Surface Machining Sculptured Surface Machining Through Force Modeling. CIRP Annals 53(1):103–106. [51] Budak E, Ozturk E, Tunc LT (2009) Modeling and Simulation of 5-Axis Milling Processes. CIRP Annals 58(1):347–350. [52] Budak E, Tunc LT, Alan S, Ozguven HN (2012) Prediction of Workpiece Dynamics and its Effects on Chatter Stability in Milling. CIRP Annals 61(1):339–342. [53] Deichmu¨ller M, Denkena B, Payrebrune KM, de, Kro¨ger M, Wiedemann S, Schro¨der A, Carstensen C (2013) Modeling of Process Machine Interactions in Tool Grinding. Process Machine Interactions, Springer, Berlin, Heidelberg143–176. [54] Denkena B, Kru¨ger M, Bachrathy D, Stepan G (2012) Model Based Reconstruction of Milled Surface Topography from Measured Cutting Forces. International Journal of Machine Tools and Manufacture 54-55:25–33. [55] Denkena B, Turger A, Behrens L, Krawczyk T (2012) Five-Axis-Grinding With Toric Tools: A Status Review. Transactions of ASME Journal of Manufacturing Science and Engineering 134(5). 54001 (6 pages). [56] Denkena B, Tracht K, Yu JH (2006) Advanced NC-Simulation based on the Dexelmodel and the HRMC-Algorithm. Production Engineering – Research and Development 13(1):91–94. [57] Du S, Surmann T, Webber O, Weinert K (2005) Formulating Swept Profiles for Five-Axis Tool Motions. International Journal of Machine Tools and Manufacture 45(7–8):849–861. [58] Ozturk E, Budak E (2010) Dynamics and Stability of 5-Axis Ball-end Milling. Transactions of ASME Journal of Manufacturing Science and Engineering 132(2). 021003 (12 pages). [59] Eksioglu C, Kilic ZM, Altintas Y (2012) Discrete-Time Prediction of Chatter Stability, Cutting Forces, and Surface Location Errors in Flexible Milling Systems. Transactions of ASME Journal of Manufacturing Science and Engineering 134(6). AN061006. [60] Engin S, Altintas Y (2001) Modeling of General Milling Operations: Part I – End Mills. International Journal of Machine Tools and Manufacture 41:2195–2212. [61] Engin S, Altintas Y (2001) Modeling of General Milling Operations: Part II – Inserted Cutters. International Journal of Machine Tools and Manufacture 41:2213–2231. [62] Erdim H, Lazoglu I, Ozturk B (2006) Feedrate Scheduling Strategies for Free-Form Surfaces. International Journal of Machine Tools and Manufacture 46:747–757.

[63] Erkorkmaz K, Altintas Y (2001) High Speed CNC System Design: Part I – Jerk Limited Trajectory Generation and Quintic Spline Interpolation. International Journal of Machine Tools and Manufacture 41(9):1323–1345. [64] Erkorkmaz K, Altintas Y (2001) High Speed CNC System Design: Part II – Modeling and Identification of Feed Drives. International Journal of Machine Tool and Manufacture 41(10):1487–1509. [65] Erkorkmaz K, Altintas Y (2001) High Speed CNC System Design: Part III – High Speed Tracking and Contouring Control of Feed Drives. International Journal of Machine Tool and Manufacture 41(11):1637–1658. [66] Erkorkmaz K, Altintas Y, Yeung CH (2006) Virtual Computer Numerical Control System. CIRP Annals 55(1):399–402. [67] Erkorkmaz K, Layegh SE, Lazoglu I, Erdim H (2013) Feedrate Optimization for Freeform Milling Considering Constraints from the Feed Drive System and Process Mechanics. CIRP Annals 62(1):395–398. [68] Eyniyan M, Altintas Y (2009) Chatter Stability of General Turning Operations with Process Damping. Transactions of ASME Journal of Manufacturing Science and Engineering 131(4):1–10. [69] Eyniyan M, Altintas Y (2010) Analytical Chatter Stability of Milling with Rotating Cutter Dynamics at Process Damping Speeds. Transactions of ASME Journal of Manufacturing Science and Engineering 132(2). 021012 (14 pages). [70] Ferry W, Altintas Y (2008) Virtual Five Axis Milling of Impellers, Part I: Mechanics of Five Axis Milling. Transactions of ASME Journal of Manufacturing Science and Engineering 130(1). 011005 (11 pages). [71] Ferry W, Altintas Y (2008) Virtual Five Axis Milling of Impellers, Part II: Feedrate Optimization of Five Axis Milling. Transactions of ASME Journal of Manufacturing Science and Engineering 130(1). 0110013 (13 pages). [72] Foley J, van Dam A, Feiner S, Hughes J (1995) Computer Graphics: Principles and Practice, Addison-Wesley. [73] Fussell BK, Jerard RB, Hemmett JG (2003) Modeling of Cutting Geometry and Forces for 5-Axis Sculptured Surface Machining. Computer-Aided Design 35:333–346. [74] Gaida WR, Rodriquez CA, Altan T, Altintas Y (1995) Preliminary Experiments for Adaptive Finish Milling of Die and Mold Surfaces with Ball-nose End Mills. Transactions of NAMRI/SME XXIII 193–198. [75] Guo G, Ranganath S, McIntosh D, Elfizy A (2008) Virtual High Performance Grinding with CBN Wheels. CIRP Annals 57(1):325–328. [76] Heo EY, Merdol D, Altintas Y (2010) High Speed Pocketing Strategy. CIRP Journal of Manufacturing Science and Technology 3(1):1–7. [77] Imani B, Elbestawi M (2001) Geometric Simulation of Ball-End Milling Operations. Journal of Manufacturing Science And Engineering 123:177–184. [78] Inasaki I (1996) Grinding Process Simulation Based on the Wheel Topography Measurement. Annals of the CIRP 45(1):347–350. [79] Inasaki I (1993) Abrasive Machining in the Future. CIRP Annals 42(2):723– 732. [80] Insperger T, Mann BP, Ste´pa´n G, Bayly PV (2003) Stability of Up-Milling and Down-Milling Part 1: Alternative Analytical Methods. International Journal of Machine Tools and Manufacture 43(1):25–34. [81] ISO 3002, Basic Quantities in Cutting and Grinding, 1982. [82] Jawahir I, Brinksmeier E, M’Saoubi R, Aspinwall D, Outeiro J, Meyer D, Umbrello D, Jayal A (2011) Surface Integrity in Material Removal Processes: Recent Advances. CIRP Annals 60(2):603–626. [83] Joliet R, Kansteiner M (2013) A High Resolution Surface Model for the Simulation of Honing Processes. Advanced Materials Research 769:69–76. [84] Kaymakci M, Kilic ZM, Altintas Y (2012) Unified Cutting Force Model for Turning, Boring, Drilling and Milling Operations. International Journal of Machine Tools and Manufacture 54/55:34–45. [85] Kersting P, Biermann D (2014) Modeling Techniques for the Simulating Workpiece Deflections in NC Milling. CIRP Journal of Manufacturing Science and Technology 7(1):48–54. [86] Kersting P, Biermann D (2012) Modeling Workpiece Dynamics Using Sets of Decoupled Oscillator Models. Journal of Machining Science and Technology 16(4):564–579. [87] Kersting P, Biermann D (2009) Simulation Concept for Predicting Workpiece Vibrations in Five-Axis Milling. Machining Science and Technology 13(2):196–209. [88] Lazoglu I, Atabey F, Altintas Y (2002) Dynamics of Boring Processes: Part III – Time Domain Modeling. Journal of Machine Tools and Manufacture 42(14):1567–1576. [89] Lazoglu I, Boz Y, Erdim H (2011) Five-Axis Milling Mechanics for Complex Free From Surfaces. CIRP Annals 60(1):117–120. [90] Lazoglu I, Manav AC, Murtezaoglu Y (2009) Tool Path Optimization for Free Form Surface Machining. CIRP Annals 58(1):101–104. [91] Lazoglu I (2003) Sculpture Surface Machining: A Generalized Model of BallEnd Milling Force System. International Journal of Machine Tools and Manufacture 43:453–462. [92] Lee S, Ko S (2002) Development of Simulation Systems for Machining Process Using Enhanced Z Map Model. Journal of Materials Processing Technology 6333:1–10. [93] Limido J, Espinsoa C, Salau¨n M, Lacome J (2007) SPH Method Applied to High Speed Cutting Modelling. International Journal of Mechanical Sciences 4(7):898–908. [94] Manav C, Bank HS, Lazoglu I (2013) Intelligent Toolpath Selection via MultiCriteria Optimization in Complex Sculptured Surface Milling. Journal of Intelligent Manufacturing 24:349–355. [95] Merdol D, Altintas Y (2004) Mechanics and Dynamics of Serrated End Mills. Transactions of ASME Journal of Manufacturing Science and Engineering 126(2):317–326. [96] Merdol D, Altintas Y (2004) Multi Frequency Solution of Chatter Stability for Low Immersion Milling. Transactions of ASME Journal of Manufacturing Science and Engineering 126(3):459–466. [97] Merdol D, Altintas Y (2008) Virtual Cutting and Optimization of Three Axis Milling Processes. International Journal of Machine Tools and Manufacture 48(10):1063–1071.

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G Model

CIRP-1256; No. of Pages 21 Y. Altintas et al. / CIRP Annals - Manufacturing Technology xxx (2014) xxx–xxx [98] Merdol DS, Altintas Y (2008) Virtual Simulation and Optimization of Milling Operations: Part I – Process Simulation. Transactions of ASME Journal of Manufacturing Science and Engineering 130(5). 051005 (10 pages). [99] Merdol DS, Altintas Y (2008) Virtual Simulation and Optimization of Milling Operations: Part II – Optimization, Feed Rate Scheduling. Transactions of ASME Journal of Manufacturing Science and Engineering 130(5). 051004 (12 pages). [100] Montgomery D, Altintas Y (1991) Mechanism of Cutting Force and Surface Generation in Dynamic Milling. Transactions of ASME Journal of Engineering for Industry 113:160–168. [101] Moufki A, Molinari A, Dudzinski D (1998) Modeling of Orthogonal Cutting with a Temperature Dependent Friction Law. Journal of Mechanics and Physics of Solids 46(10):2103–2138. [102] Ozguven HN (1990) Structural Modifications Using Frequency Response Functions. Mechanical Systems and Signal Processing 4(1):53–63. [103] Ozlu E, Budak E (2007) Analytical Modeling of Chatter Stability in Turning and Boring Operations. Part I: Model Development. Transactions of ASME Journal of Manufacturing Science and Technology 129:726–732. [104] Ozturk E, Budak E (2007) Modeling of 5-Axis Milling Process. Journal of Machining Science and Technology 11(3):287–311. [105] Ozturk E, Tunc LT, Budak E (2011) Analytical Methods for Increased Productivity in 5-Axis Ball-End Milling. International Journal of Mechatronics and Manufacturing Systems 4(3/4):238–265. [106] Ozturk E, Tunc LT, Budak E (2009) Investigation of Lead and Tilt Angle Effects in 5-Axis Ball-End Milling Processes. International Journal of Machine Tools and Manufacture 49(14):1053–1062. [107] Pritschow G, Altintas Y, Javone F, Koren Y, Mitsuishi M, Takata T, Van Brussel H, Weck M, Yamazaki K (2001) Open Controller Architecture – Past, Present and Future. CIRP Annals 50(2):446–463. [108] Requicha A, Rossignac J (1992) Solid Modeling and Beyond. IEEE Computer Graphics and Applications 12(5):31–44. [109] Roukema JC, Altintas Y (2007) Generalized Modeling of Drilling Vibrations, Part I: Time Domain Model of Drilling Kinematics, Dynamics and Hole Formation. International Journal of Machine Tools and Manufacture 47(9):1455–1473. [110] Roukema JC, Altintas Y (2007) Generalized Modeling of Drilling Vibrations, Part II: Chatter Stability in Frequency Domain. International Journal of Machine Tools and Manufacture 47(9):1474–1485. [111] Sencer B, Altintas Y, Croft E (2008) Feed Optimization for 5 Axes CNC Machine Tools with Drive Constraints. International Journal of Machine Tools and Manufacture 48(7–8):733–745. [112] Siebrecht T, Biermann D, Ludwig H, Rausch S, Kersting P, Blum H, Rademacher A (2014) Simulation of Grinding Processes Using Finite Element Analysis and Geometric Simulation of Individual Grains. Production Engineering Research and Development 8(3):345–353. [113] Siebrecht T, Rausch S, Kersting P, Biermann D (2014) Grinding Process Simulation of Free-Formed WC-Co Hard Material Coated Surfaces on Machining Centers Using Poisson-Disk Sampled Dexel Representations. CIRP Journal of Manufacturing Science and Technology. [114] Smith S, Tlusty J (1991) An Overview of Modeling and Simulation of the Milling Process. Transactions of ASME Journal of Engineering for Industry 113(2):169–175. [115] Spence A, Altintas Y (1994) A Solid Modeller Based Milling Process Simulation and Planning System. Transactions of ASME Journal of Engineering for Industry 116:61–69. [116] Spence A, Altintas Y (1991) CAD Assisted Adaptive Control for Milling. Transactions of ASME Journal of Dynamic Systems Measurement and Control 113:444–450. [117] Spence A, Altintas Y, Kirkpatrick D (1990) Direct Calculation of Machining Parameters from a Solid Model. Journal of Computers in Industry 14(4):271–280. [118] Surmann T, Biermann D (2008) The Effect of Tool Vibrations on the Flank Surface Created by Peripheral Milling. CIRP Annals 57(1):375–378.

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[119] Surmann T, Ungemach E, Zabel A, Joliet R, Schro¨der A (2011) Simulation of the Temperature Distribution in NC-Milled Workpieces. Advanced Materials Research 223:222–230. [120] Tekeli A, Budak E (2005) Maximization of Chatter Free Material Removal Rate in End Milling Using Analytical Methods. Journal of Machining Science and Technology 9:147–167. [121] To¨nshoff HK, Peters J, Inasaki T, Paul T (1992) Modelling and Simulation of Grinding Processes. CIRP Annals 41(2):677–688. [122] To¨nshoff HK, Denkena B, Bo¨ß V, Urban B (2002) Automated Finishing of Dies and Molds. Production Engineering – Research and Development in Germany 9/2:1–4. [123] To¨nshoff HK, Spintig W, Koenig W (1994) Machining of Holes – Developments in Drilling Technology. Annals of CIRP 43/2:551–561. [124] Tunc LT, Budak E (2012) Effect of Cutting Conditions and Tool Geometry on Process Damping in Machining. International Journal of Machine Tools and Manufacture 57:10–19. [125] Tunc LT, Budak E (2009) Identification and Modeling of Process Damping in Milling. Transactions of ASME Journal of Manufacturing Science and Engineering 131(5). http://dx.doi.org/10.1115/1.4000170. [126] Tunc LT, Budak E (2009) Extraction of 5-Axis Milling Conditions from CAM Data for Process Simulation. International Journal of Advanced Manufacturing Technology 43(5–6):538–550. [127] Tuysuz O, Altintas Y, Feng HS (2012) Prediction of Cutting Forces in Three and Five-Axis Ball-End Milling with Tool Indentation Effect. International Journal of Machine Tools and Manufacture vol. 66:66–81. [128] Ungemach E, Surmann T, Zabel A (2008) Dynamics and Temperature Simulation in Multi-Axis Milling. Advance Materials Research 43:89–96. [129] Van Hook T (1986) Real-Time Shaded NC Milling Display. ACM SIGGRAPH Computer Graphics 20(4):15–20. [130] Wang W, Wang K (1986) Geometric Modeling for Swept Volume of Moving Solids. Computer Graphics and Applications 6(12):8–17. [131] Weck M, Altintas Y, Beer C (1994) CAD Assisted Chatter Free NC Tool Path Generation in Milling. International Journal of Machine Tools and Manufacture 34(6):879–891. [132] Weinert K, Blum H, Jansen T, Rademacher A (2007) Simulation Based Optimization of the NC-Shape Grinding Process with Toroid Grinding Wheels. Production Engineering Research and Development 1(3):245–252. [133] Weinert K, Du S, Damm P, Stautner M (2004) Swept Volume Generation for the Simulation of Machining Processes. International Journal of Machine Tools and Manufacture 44:617–628. [134] Weinert K, Enselmann A, Friedhoff J (1997) Milling Simulation for Process Optimization in the Field of Die and Mould Manufacturing. CIRP Annals 46(1):325–328. [135] Weinert K, Kersting P, Surmann T, Biermann D (2008) Modeling Regenerative Workpiece Vibrations in Five-Axis Milling. Production Engineering Research and Development 2(3):255–260. [136] Xiaoliang J, Altintas Y (2010) Slip-line Field Model of Micro-Cutting Process with Round Tool Edge Effect. Journal of Materials Processing Technology 211:339–355. [137] Xiaoliang J, Altintas Y (2012) Prediction of Micro-Milling Forces with Finite Element Method. Journal of Materials Processing Technology 212(3):542–552. [138] Xiaoliang J, Altintas Y (2013) Chatter Stability Model of Micro-Milling With Process Damping. Transactions of ASME Journal of Manufacturing Science and Engineering 135(3):031011. [139] Yeung CH, Altintas Y, Erkorkmaz K (2006) Virtual CNC System – Part I: System Architecture. International Journal of Machine Tools and Manufacture 46(10):1107–1123. (9 pages). [140] Yeung CH, Altintas Y, Erkorkmaz K (2006) Virtual CNC System – Part II: High Speed Contouring Application. International Journal of Machine Tools and Manufacture 46(10):1124–1138. [141] Yuen A, Zhang K, Altintas Y (2013) Smooth Trajectory Generation for FiveAxis Machine Tools. International Journal of Machine Tools and Manufacture Vol. 71:11–19.

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