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Optimization of 3D surface roughness induced by milling operation for adhesive-sealing
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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 71 (2018) 279–284 www.elsevier.com/locate/procedia
4th 4th CIRP CIRP Conference Conference on on Surface Surface Integrity Integrity (CSI (CSI 2018) 2018)
Optimization 3D surface induced by Designroughness Conference, May 2018, Nantes, France operation Optimization of of28th 3DCIRP surface roughness induced by milling milling operation for for adhesive-sealing adhesive-sealing A new methodology to analyze the functional and physical architecture of a,b a,b a, a,b c c a,b,an a,b, Lixin Wang c, Hongbo Shun Liu Zhang *, Zhao existing for assembly product family identification Shun products Liua,b,, Sun Sun Jin Jin , Xueping Xueping Zhanga,oriented *, Kun Kun Chen Chen , Lixin Wang , Hongbo Zhaoc a aState
Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China State Key b Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China bShanghai Key Laboratory of Digital Manufacture for Thin-Walled Structures, Shanghai Jiao Tong University, Shanghai 200240, China Shanghai cKey Laboratory of Digital Manufacture for Thin-Walled Structures, Shanghai Jiao Tong University, Shanghai 200240, China cSAIC-GM Corporation Limited, PTME department, Shenjiang Rd.1500, Pudong District, 200000 Shanghai, China SAIC-GM Corporation Limited, PTME department, Shenjiang Rd.1500, Pudong District, 200000 Shanghai, China École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France * Corresponding author. Tel.: +86-21-34206799 ; fax: +86-21-34206799. E-mail address: [email protected] * Corresponding author. Tel.: +86-21-34206799 ; fax: +86-21-34206799. E-mail address: [email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract
Abstract In automotive engine manufacturing, the adhesive-sealing performance between two machined surfaces is closely related with machined In automotive engine manufacturing, the adhesive-sealing performance between two machined surfaces is closely related with machined surface topography induced by face milling. Joint researches in machining and adhesive are needed to enhance the sealing performance. This surface topography induced by face milling. Joint researches in machining and adhesive are needed to enhance the sealing performance. This Inpaper today’s business environment, the trendunderstanding towards moreofproduct variety customization unbroken. Due to this3D development, theadhesiveneed of aims to enhance a comprehensive the impact of and milling parametersisand machined surface roughness on paper aims to enhance a comprehensive understanding of the impact of milling parameters and machined surface 3D roughness on adhesiveagile andperformance. reconfigurable production systems emerged to cope with various aproducts and product families. To design and optimize production sealing Three experiments were conducted in this research: set of milling tests through design of experiment (DoE) to obtain sealing performance. Three experiments were conducted in this research: a set of milling tests through design of experiment (DoE) to obtain systems welldifferent as to choose thequalities, optimal 3D product matches, of product analysis methods are needed. Indeed, of the known methods aimand to surfacesaswith surface measurement machined surface topographies to identify themost surface roughness parameters, surfaces with different surface qualities, 3D measurement of machined surface topographies to identify the surface roughness parameters, and analyze product or one product on the physical level. Different product families,Then however, may differ largely in terms of the number and tensile atests to characterize the family adhesive-sealing performance of machined surfaces. the relationships of surface roughness parameters, tensile tests to characterize the adhesive-sealing performance of machined surfaces. Then the relationships of surface roughness parameters, nature components. fact impedes efficient comparison and choice of appropriate product family combinations for the (RSM) production millingofparameters andThis functional sealinganperformance were modeled and optimized by means of response surface methodology and milling parameters and functional sealing performance were modeled and optimized by means of response surface methodology (RSM) and system. new methodology is proposed analyzemanufactures existing products in viewapproach of their functional andmilling physicalparameters architecture. aim is to surface cluster geneticAalgorithm (GA). This research to provides a reliable on selecting andThe monitoring genetic algorithm (GA). This research provides manufactures a reliable approach on selecting milling parameters and monitoring surface these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable roughness. roughness. assembly systems. Based on Datum Chain, © 2018 The Authors. Published by Flow Elsevier B.V.the physical structure of the products is analyzed. Functional subassemblies are identified, and © 2018 The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. B.V.This is an open access article under the CC BY-NC-ND license a(https://creativecommons.org/licenses/by-nc-nd/4.0/) functional analysis is performed.ofMoreover, a hybrid functional and CIRP physical architecture graph (HyFPAG) is the output which depicts the Peer-review under responsibility the scientific committee of the 4th Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). similarity product under families by providing design support to both,ofproduction system plannersonand product designers. An illustrative Selection between and peer-review responsibility of the scientific committee the 4th CIRP Conference Surface Integrity (CSI 2018). Keywords: 3D surface roughness optimization; adhesive-sealing example of aMilling; nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of Keywords: Milling; 3D surface roughness optimization; adhesive-sealing thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. © 2017 The Authors. Published by Elsevier B.V. 1. Introduction theDesign influences of 2018. material properties, adhesive bonding Peer-review under responsibility of the scientific committee of the 28th CIRP Conference 1. Introduction the influences of material properties, adhesive bonding Keywords: Assembly; method; Family identification Adhesive has Design high bonding reliability and perfect
sealing Adhesive has high bonding reliability and perfect sealing quality and it is widely used between two joint-machined quality and it is widely used between two joint-machined surfaces for bonding them together or achievement the surfaces for bonding them together or achievement the adhesive sealing requirement, such as in car engine, airplane sealing requirement, such as in car engine, airplane 1.adhesive Introduction and other industry parts [1-3]. For example, adhesive has and other industry parts [1-3]. For example, adhesive has been applied for sealing between two machined surfaces in been for sealing between two in machineddomain surfaces ofin Dueapplied to engine the fast development automotive manufacturing. And thethe adhesive-sealing automotive engine manufacturing. And of the digitization adhesive-sealing communication and an ongoing trend and quality directly related with engine service performance, quality directly related with engine service performance, digitalization, manufacturing enterprises are facing important safety and energy consumption. Thus it is important to safety andinenergy Thus it is aimportant to challenges today’sconsumption. market quality environments: improve the adhesive-sealing between twocontinuing machined improve towards the adhesive-sealing quality development between two times machined tendency reduction of product and surfaces by milling. surfaces by milling. shortened product lifecycles. In addition, there is ansurfaces increasingis The adhesive performance of two joint The ofadhesive performance ofthetwo joint surfaces demand customization, being at same time in a globalis mechanically complex. And the influence factors derivative mechanically complex. And the influence factorsThis derivative competition with competitors all over the world. trend, not only from the design of adhesive process but also from the not only from the design of adhesive process but alsotofrom the which is inducing the development from macro micro service environment [4]. In this field, the main task is to service environment [4]. In this field, the main task is to markets, results in diminished lot sizes due to augmenting enhance the adhesive quality through process design before enhancevarieties the adhesive quality through process design before product (high-volume low-volume production) [1]. application. Many researches to have been conducted to study application. researches variety have been conducted study To cope with Many this augmenting as well as to betoable to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The Authors. Published Elsevier B.V. knowledge production is important toby a precise 2212-8271 © 2018 TheitAuthors. Published byhave Elsevier B.V.
process, geographer and structure of adhesive layer and joint process, geographer and structure of adhesive layer and joint in macro scale level, and surface texture in micro scale level in macro scale level, and surface texture in micro scale level [5, 6] on the adhesive quality. Several useful strategies have [5, 6] on the adhesive quality. Several useful strategies have been proposed to improve the shear strength of adhesive [7, 8]. been proposed to improve the shear strength of adhesive [7, 8]. By design of joint configuration, non-flat interface profile can Bythe design of joint configuration, non-flatmanufactured interface profile can of product range and characteristics and/or improve the adhesive quality that the shear strength of reverse improve the quality that the shear of reverse assembled in adhesive this system. In this context, the strength main challenge in bent joint and wavy-lap joint are higher than traditional flatbent joint and analysis wavy-lapisjoint are higher than traditional flatmodelling now not only to cope with single lap joint [9, 10]. Through pre-treatments on surfaces, the lap joint a[9, 10]. product Throughrange pre-treatments on surfaces, the products, limited or existing product surface roughness is thus increased and then the families, adhesive surface roughness isanalyze thus increased and then the to adhesive but also to be able to and to compare products define strength is enhanced [11, 12]. It is well known that the quality strength is enhanced [11,can 12]. is well known that theexisting quality new product families. beItobserved that classical of machined surface It has considerable influence on adhesiveof machined surface has considerable influence onoradhesiveproduct families are regrouped in function of clients features. sealing performance especially in micro-level the surface sealing performance especially infamilies micro-level the to surface However, product are hardly find. roughnessassembly and hasoriented not been systemically and sufficiently roughness and has not level, been products systemically and sufficiently On the product family differ mainly in two studied yet [13-15]. For engine manufacturing, the surfaces to studied yet [13-15]. For engine manufacturing, the and surfaces to main characteristics: (i) the number of components (ii) the be adhesively sealed are machined by milling operations and be adhesively sealed aremechanical, machined by milling electronical). operations and type of components (e.g. electrical, the surface qualities are determined by milling parameters [16]. theClassical surface qualities are determined by milling parameters [16]. methodologies considering mainly single products It thus makes a significance to analytically investigate the It thus makes a significance to analytically investigate the or solitary, already existing product families analyze effects of machined surface qualities on the reliabilitythe of effects structure of machined surface level qualities on the reliability of product on a physical (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). 2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 10.1016/j.procir.2018.05.011
Shun Liu et al. / Procedia CIRP 71 (2018) 279–284 Shun Liu et al. / Procedia CIRP 00 (2018) 000–000
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adhesive joint surface, and then provides a theoretical support for the design of face machining process. In this paper, three experiments have been conducted to study the adhesive-sealing performance related with milling parameters and also the machined surface topography induced by face milling. Through these experiments, the relationships of surface roughness parameters, milling parameters and functional sealing performance are modeled by response surface methodology (RSM). And then the effects of the machined surface topography and milling parameters on the reliability of the adhesive joint surface are systemically investigated. At last, optimized surface qualities and milling strategies are obtained based on these models with genetic algorithm (GA). Thus it provides a systemically thinking to improve the performance of adhesive bonding and sealing for its industry implementation; and also a theoretical support for the selection of face topographers and parameters’ optimization of face milling operation.
D6.0mm×12mm×57mm, side rake angle 15.0deg and back rake angle 30.0deg. The grade type is carbide PVD. The specimen to be machined was mounted on a case aluminum fixture. The material of specimen is aluminum alloy 5054 and the dimensions of blank are 90mm×25mm×10mm. The area to be machined is 35mm×25mm at one head of the specimen which is designed as the adhesive contact surface.
Nomenclature
f ap
Spindle speed Feed rate The depth of cut
ae
The radial depth of cut
s P
Shear strength Tensile load Joint overlap area
v
As Ra Sa Y x b Ypre Ymes
2D surface roughness 3D surface roughness Desired response Independent input variable Unknown coefficient Predicted values Experimental measured results
2. Experimental setup and procedure In this section, three experiments have been conducted: (1) The parameters of face milling operations for aluminum alloy was selected and then applied on a high speed machining center to machined samples’ surfaces with different qualities for the adhesive analysis; (2) The 3D texture of machined surfaces were measured with 3D surface optical profile and represented by cloud points, then the surface parameters were analyzed based on these cloud points; (3) The adhesive performance test was taken through tensile test. 2.1. Face milling experimental design Fig. 1 shows the overview of the face milling experimental setup. Face milling was implemented to produce specimens with different machined surfaces on a machining center (DMG HSC-75). The helical milling tool with two teeth (ID S4520660E2CZ2.0-HEMI, SECO) was installed for end milling process. The geography dimensions of milling tool are
Fig. 1. Overview of face milling experimental setup: (a) overview; (b) milling tool; (c) fixture and specimen.
All the milling operations were performed in two stages. Firstly, a rough milling process was performed before the finish milling in order to reduce the effect of initial rough surface on surface quality. The rough milling process for each specimen was conducted with coolant. The milling parameters, such as the radial depth of cut, the feed rate, and spindle speed, were chosen suitably to have a good surface quality and high machining efficiency simultaneously. The total depth of cut of each operation was adjusted according to the corresponding cutting depth of finish milling to maintain that after the finish milling the residual height of each specimen at the machining head maintains the same. Then the finish milling process was performed under dry conditions. Each specimen had different finishing milling parameters thus to produce different surfaces. Table 1. Parameters of face milling operations and its levels. Parameters
Level 1
Level 2
Level 3
Level 4
A
Spindle speed (r/min)
6000
8000
10000
12000
B
Feed rate (mm/min)
600
1200
2400
3600
C
The depth of cut (mm)
0.1
0.1
0.1
0.1
D
Radial depth of cut (mm)
2.6
3.0
3.6
4.0
Orthogonal array was used for the experimental design of finish milling in order to study how the face milling parameters affect the output factors and also to obtain optimized milling parameters and surface qualities to achieve higher adhesive sealing quality. As shown in Table 1, the face milling parameters concluded the spindle speed, feed rate, the radial depth of cut and the depth of cut. And each of them had four levels except the depth of cut which was set with the same value to guarantee the same residual height of machined specimen. The experimental design adopted Taguchi L16(4^3)
Shun Liu et al. / Procedia CIRP 71 (2018) 279–284 Shun Liu et al. / Procedia CIRP 00 (2018) 000–000
orthogonal method which consisted of 16 experiments with different milling parameters. Each experiment conducted twice to produce a pair of specimens with same surfaces in order to fabricate a single-lap joint for tensile test. 2.2. Surface measurement setup 3D surface optical profiler (KEYENCE KS-1100) and stereomicroscope (ZEISS Stemi-2000) were implemented to obtain the topographies of machined specimens as shown in Fig. 2. Laser head (LK-G10) was implemented on 3D surface optical profiler to capture the point cloud of machined surface area. The measurement area was 7000μm×5000μm with a scanning step of 5μm on a scanning speed 5000μm/s. Then topography of each specimen was demonstrated by a point cloud with 1001×1401 matrix. With the point cloud matrix, 3D surface parameters can be obtained software MATLAB R2014a. All the specimens were also measured with stereomicroscope with ×40 magnification in order to obtain the micro textures. The 2D surface roughness was measured with a surface roughness tester (Mitutoyo SJ-210).
281 3
To prepare the test samples, a couple of machined specimens milled by the same parameters in face milling experiments were boned together as a single lap joint sample. All the sixteen samples were bonded at the same time. The sealing adhesive applied is the room temperature vulcanized (RTV) silicone rubber (Loctite5970). After the fabrication procedures, test samples were stored in controlled chamber at temperature of 23℃ and relative humidity of 50%RH, and then they were tested at same curing time of 21 days. All the samples were prepared with same procedure at the same time. All the obtained samples were tested using Zwick/Roell Z010 testing machine at room temperature for the determination of joint shear strength. The displacement rate was constant (10mm/min). The shear strength is evaluated by:
s
P As
(1)
2.4. Experimental procedure Input face milling variables Spindle speed
Feed rate
Depth of cut
Radial depth of cut
Face Milling Experiment B
A Surface Measurement Experiment
(a) (b) Fig. 2. Measurement setup of: (a) optical profiler; (b) stereomicroscope.
2.3. Tensile test
2D surface roughness
Single Lap Joint Tensile Test
3D surface roughness
Shear strength
Output measurement variables Fig. 4. Experimental design.
To evidence the effect of the surface qualities on the toughening properties of the adhesives between two machined surfaces, the single lap joint test was carried out to determinate the joint shear strength (or saying the failure load because all the samples were test with a same joint overlap area). In Fig. 3 the structure of single-lap joint, the sizes of adherent and adhesive were defined considering the geometries of the machined samples and the working conditions of adhesive sealing between two machined parts. The thickness of the adhesive was chosen as constant 0.1mm to make sure the failure type was not substrate failure. 9mm
10mm
RTV, 0.1mm
25mm 30mm
Fig. 3. Structure of single lap joint sample.
In this study, all the specimens are machined by face milling experiment with different parameters determined by orthogonal experiment method. The four experimental variables are the spindle speed, feed rate, depth of cut and radial depth of cut. Each variable had four levels and totally sixteen pairs of specimens were produced in the experiment. Then the surfaces were measured and surface quality indicators were calculated. The typical parameters used in this study are 2D/3D surface roughness derived from the direct outcome measures. A total of sixteen combinations were tested in single lap joint tensile test to obtain the shear strength. The procedures of experimental design are shown in Fig. 4. After the experiments, the relationship between input parameters (face milling variables) and output variables (surface quality parameters and tensile test outcomes) can be modeled by empirical modeling approach of RSM. In Fig. 4, Leg A and Leg B highlight the two response functions. Then the established response functions (shown as Leg A) can be used to maximum the shear strength of functional performance by determining the approximation face milling parameters using GA. If the optimized face milling parameters are
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obtained, then with the response functions shown in Leg B, surface parameters of machined surface that have strong shear strength and perfect adhesive performance can be determined. 3. Experimental results and modeling using RSM 3.1. Experimental results Sixteen pairs of specimens were successfully machined by face milling as shown in Fig. 5. From the figure, it is seen that all the surfaces have different textures in macroscopic scale view. It suggested that face milling with different parameters are capable of producing surfaces with different qualities.
2D/3D surface roughness are [0.365, 1.668] and [1.838, 4.337], respectively. The experimental results of shear strength are shown in Fig. 8. And the relationship between shear strength and surface parameters of 2D/3D surface roughness are shown in Fig. 7. It can be seen that the relationship between shear strength and surface parameters are quite complicated. The nonlinear relations donate that the shear strength (adhesive performance) differs with surface topography quality and cannot be determined directly by single surface parameter. And the lower or higher surface roughness does not mean higher shear strength. Thus it is be of great importance to build a model to describe the relationship between shear strength and surface qualities through a bridge such as face milling parameters which can connect the two output variables together.
(a) Fig. 5. Sixteen pairs of specimens with machined surfaces by milling.
(b)
Fig. 7. The relationship between shear strength and surface parameters of: (a) 2D surface roughness; (b) 3D surface roughness
3.2. Regression analysis using RSM
7mm×5mm
1000μm
In this study, the collection of experimental data adopts a standard RSM design and the desired responses and independent input variables are represented by a quadratic regression model with a general second-order polynomial regression formula given in Eq. (2). k
(a)
k
k
Y f X b0 bi xi bij xi x j i 1
(2)
i 1 j i
bi , bij Where Y is the desired response, Y [ s , Ra , Sa ]t ; b0 ， are the unknown coefficients, each of them is a 3×1 vector corresponding to the desired response; xi and x j are the 7mm×5mm
1000μm
(b) Fig. 6. Surface topography measured by 3D surface optical profiler in sampling area 7mm×5mm and stereomicroscope (40×) with different surface roughness in experiment: (a) No. 3, Ra 1.571μm; (b) No. 14, Ra 0.65μm.
independent input variables, X [v, f , ae , a p ] . The shear strength model is given by Eq. (3).
s 0.633+104 v 8.8 104 f 1.130ae 108 vf 4.87 105 vae +6.30 105 fae 9
2
8
2
2 e
(3)
5.94 10 v 3.0 10 f 0.10a The measurement results and surface parameters obtained are shown in Fig. 6, Fig. 9 and Fig. 10. Fig. 6 shows the The quadratic model of 2D surface roughness can be typical surface topographies with different surface qualities by written as different milling operations. It is observed that the surface texture has more obvious Ra 2.3932 104 v 1.43 103 f 4.95 102 ae grooves and peaks with larger surface roughness (Ra 1.571μm) 108 vf 7.66 105 vae 1.16 104 fae (4) in Fig. 6 (a) when compared to that of the texture with lower 8 2 7 2 2 2 surface roughness (Ra 0.65μm) in Fig. 6 (b). Surface 10 v 1.25 10 f 9.34 10 ae parameters are obtained by post-process of point cloud with MATLAB. From the table, as it can be seen that the range of The 3D surface roughness model is given below in Eq. (5).
Shun Liu et al. / Procedia CIRP 71 (2018) 279–284 Shun Liu et al. / Procedia CIRP 00 (2018) 000–000
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Sa 3.306 103 v 3.19 103 f 5.344ae 108 vf 1.48 104 vae 4.73 104 fae 8
2
7
2
(5)
2 e
10 v 2.85 10 f 0.827a
The predicted values obtained from the quadratic models were compared with experimental measured results. The percentage error is calculated by:
Ypre Ymes Error % 100 Ymes
(6)
The relationship between the predicted and measured results was shown in Fig. 8, Fig. 9, and Fig. 10. Fig. 10. Comparison between predicted and measured values for 3D surface roughness.
4. Optimization and discussion
Fig. 8. Comparison between predicted and measured values for shear strength.
In this study, the main work concerned with maximization of shear strength for improving adhesive functional performance was subjected to design parameters of face milling. And then with the optimized face milling parameters, the desired quality parameters of machined surface was derived and used for the monitoring of machined surface quality variation in manufacturing. Then the optimization model for face milling parameters under special requirements, which concludes the objective function for shear strength and the constraint conditions of 2D/3D surface roughness for surface qualities and limitation ranges of optimal variables, is expressed as following
maximize s =f X t lb Ra , Sa ub s t . . X X X
(7)
Where, lb and ub are the lower and upper boundary of surface quality requirements, lb= 0.3,1.5 and ub= 2.0,5.0 , t
t
respectively. X and X are the lower and upper range limitation
of
X =5000, 400,0.1, 2.4
t
variables,
and
X 13000, 4000,0.1, 4.2 , respectively. t
Fig. 9. Comparison between predicted and measured values for 2D surface roughness.
The errors between predicted and measured results of 2D/3D surface roughness are within an acceptable range. While the relative errors of shear strength are a bit large especially in sixth and eleventh experiment. The larger gap comes from the error and complexity of tensile experiment, but all the percentage errors are acceptable while excluding sixth and eleventh experiment. Thus the established quadratic regression models are validated for the prediction of response variables with the input face milling parameters.
The constrained optimization problem was solved by GA. The GA is programmed with MATLAB R2014a for optimizing the face milling parameters. The relationship between fitness values and evolutionary generations is shown in Fig. 11. It shows that the optimization process convergences after 87th generation which shows the fast rate of convergence and excellent fitness searching capability of the applied algorithm. After the optimization run, the optimized shear strength is represented as 2.124 N/mm2 and the corresponding optimized
values of design parameters are X=13000, 4000,0.1,3.748 . t
Then the results of desired quality parameters of machined surface are calculated with Eq. (4) and Eq. (5), Ra =1.226 and
Sa 3.242 , respectively. It indicates that the largest shear
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strength does not appear with the largest or smallest surface roughness but in a suitable roughness range of surface quality.
Acknowledgements This research was supported from the National Natural Science Foundation of China under the key project No.51535007, the National Natural Science Foundation of China under the project No.51675339, and Shanghai GM PTME company under the project No. PS23005233. References
Fig. 11. The fitness and mean values of objective variable during optimization process change along with evolutionary generations.
5. Conclusions This study investigated the effects of the spindle speed, feed rate, depth of cut and radial depth of cut on surface quality parameters (2D/3D surface roughness) and adhesive functional performance (shear strength) using orthogonal experimental design method. The corresponding models were established with RSM and then responses were optimized using GA. The results obtained in the three experimental studies, including face milling experiment, surface measurement experiment and tensile test, were used to establish quadratic models. It was found that the two models can be used for the prediction of 2D/3D surface roughness and tensile strength with acceptable errors. It found that the shear strength is nonlinear with single surface parameters such as 2D/3D surface roughness, and it is reasonable to connect these two variables through face milling parameters. The optimal values for maximum tensile shear strength are the spindle speed of 13000r/min, feed rate of 4000mm/min, and radial depth of cut of 3.748mm. And the 2D/3D surface roughness values subject to the optimal face milling parameters are 1.226μm and 3.242μm, respectively. Thus they can be used to monitor the quality variation of manufacturing. The proposed methodology in this study is useful to enhance adhesive functional performance by optimization design of face milling parameters and surface parameters. It can be easily employed in real applications without computational complexity.
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Procedia CIRP 00 (2017) 000–000 Procedia CIRP 71 (2018) 279–284 www.elsevier.com/locate/procedia
4th 4th CIRP CIRP Conference Conference on on Surface Surface Integrity Integrity (CSI (CSI 2018) 2018)
Optimization 3D surface induced by Designroughness Conference, May 2018, Nantes, France operation Optimization of of28th 3DCIRP surface roughness induced by milling milling operation for for adhesive-sealing adhesive-sealing A new methodology to analyze the functional and physical architecture of a,b a,b a, a,b c c a,b,an a,b, Lixin Wang c, Hongbo Shun Liu Zhang *, Zhao existing for assembly product family identification Shun products Liua,b,, Sun Sun Jin Jin , Xueping Xueping Zhanga,oriented *, Kun Kun Chen Chen , Lixin Wang , Hongbo Zhaoc a aState
Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China State Key b Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China bShanghai Key Laboratory of Digital Manufacture for Thin-Walled Structures, Shanghai Jiao Tong University, Shanghai 200240, China Shanghai cKey Laboratory of Digital Manufacture for Thin-Walled Structures, Shanghai Jiao Tong University, Shanghai 200240, China cSAIC-GM Corporation Limited, PTME department, Shenjiang Rd.1500, Pudong District, 200000 Shanghai, China SAIC-GM Corporation Limited, PTME department, Shenjiang Rd.1500, Pudong District, 200000 Shanghai, China École Nationale Supérieure d’Arts et Métiers, Arts et Métiers ParisTech, LCFC EA 4495, 4 Rue Augustin Fresnel, Metz 57078, France * Corresponding author. Tel.: +86-21-34206799 ; fax: +86-21-34206799. E-mail address: [email protected] * Corresponding author. Tel.: +86-21-34206799 ; fax: +86-21-34206799. E-mail address: [email protected]
Paul Stief *, Jean-Yves Dantan, Alain Etienne, Ali Siadat
* Corresponding author. Tel.: +33 3 87 37 54 30; E-mail address: [email protected]
Abstract Abstract
Abstract In automotive engine manufacturing, the adhesive-sealing performance between two machined surfaces is closely related with machined In automotive engine manufacturing, the adhesive-sealing performance between two machined surfaces is closely related with machined surface topography induced by face milling. Joint researches in machining and adhesive are needed to enhance the sealing performance. This surface topography induced by face milling. Joint researches in machining and adhesive are needed to enhance the sealing performance. This Inpaper today’s business environment, the trendunderstanding towards moreofproduct variety customization unbroken. Due to this3D development, theadhesiveneed of aims to enhance a comprehensive the impact of and milling parametersisand machined surface roughness on paper aims to enhance a comprehensive understanding of the impact of milling parameters and machined surface 3D roughness on adhesiveagile andperformance. reconfigurable production systems emerged to cope with various aproducts and product families. To design and optimize production sealing Three experiments were conducted in this research: set of milling tests through design of experiment (DoE) to obtain sealing performance. Three experiments were conducted in this research: a set of milling tests through design of experiment (DoE) to obtain systems welldifferent as to choose thequalities, optimal 3D product matches, of product analysis methods are needed. Indeed, of the known methods aimand to surfacesaswith surface measurement machined surface topographies to identify themost surface roughness parameters, surfaces with different surface qualities, 3D measurement of machined surface topographies to identify the surface roughness parameters, and analyze product or one product on the physical level. Different product families,Then however, may differ largely in terms of the number and tensile atests to characterize the family adhesive-sealing performance of machined surfaces. the relationships of surface roughness parameters, tensile tests to characterize the adhesive-sealing performance of machined surfaces. Then the relationships of surface roughness parameters, nature components. fact impedes efficient comparison and choice of appropriate product family combinations for the (RSM) production millingofparameters andThis functional sealinganperformance were modeled and optimized by means of response surface methodology and milling parameters and functional sealing performance were modeled and optimized by means of response surface methodology (RSM) and system. new methodology is proposed analyzemanufactures existing products in viewapproach of their functional andmilling physicalparameters architecture. aim is to surface cluster geneticAalgorithm (GA). This research to provides a reliable on selecting andThe monitoring genetic algorithm (GA). This research provides manufactures a reliable approach on selecting milling parameters and monitoring surface these products in new assembly oriented product families for the optimization of existing assembly lines and the creation of future reconfigurable roughness. roughness. assembly systems. Based on Datum Chain, © 2018 The Authors. Published by Flow Elsevier B.V.the physical structure of the products is analyzed. Functional subassemblies are identified, and © 2018 The Authors. Authors. Published Published by by Elsevier Elsevier Ltd. B.V.This is an open access article under the CC BY-NC-ND license a(https://creativecommons.org/licenses/by-nc-nd/4.0/) functional analysis is performed.ofMoreover, a hybrid functional and CIRP physical architecture graph (HyFPAG) is the output which depicts the Peer-review under responsibility the scientific committee of the 4th Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). similarity product under families by providing design support to both,ofproduction system plannersonand product designers. An illustrative Selection between and peer-review responsibility of the scientific committee the 4th CIRP Conference Surface Integrity (CSI 2018). Keywords: 3D surface roughness optimization; adhesive-sealing example of aMilling; nail-clipper is used to explain the proposed methodology. An industrial case study on two product families of steering columns of Keywords: Milling; 3D surface roughness optimization; adhesive-sealing thyssenkrupp Presta France is then carried out to give a first industrial evaluation of the proposed approach. © 2017 The Authors. Published by Elsevier B.V. 1. Introduction theDesign influences of 2018. material properties, adhesive bonding Peer-review under responsibility of the scientific committee of the 28th CIRP Conference 1. Introduction the influences of material properties, adhesive bonding Keywords: Assembly; method; Family identification Adhesive has Design high bonding reliability and perfect
sealing Adhesive has high bonding reliability and perfect sealing quality and it is widely used between two joint-machined quality and it is widely used between two joint-machined surfaces for bonding them together or achievement the surfaces for bonding them together or achievement the adhesive sealing requirement, such as in car engine, airplane sealing requirement, such as in car engine, airplane 1.adhesive Introduction and other industry parts [1-3]. For example, adhesive has and other industry parts [1-3]. For example, adhesive has been applied for sealing between two machined surfaces in been for sealing between two in machineddomain surfaces ofin Dueapplied to engine the fast development automotive manufacturing. And thethe adhesive-sealing automotive engine manufacturing. And of the digitization adhesive-sealing communication and an ongoing trend and quality directly related with engine service performance, quality directly related with engine service performance, digitalization, manufacturing enterprises are facing important safety and energy consumption. Thus it is important to safety andinenergy Thus it is aimportant to challenges today’sconsumption. market quality environments: improve the adhesive-sealing between twocontinuing machined improve towards the adhesive-sealing quality development between two times machined tendency reduction of product and surfaces by milling. surfaces by milling. shortened product lifecycles. In addition, there is ansurfaces increasingis The adhesive performance of two joint The ofadhesive performance ofthetwo joint surfaces demand customization, being at same time in a globalis mechanically complex. And the influence factors derivative mechanically complex. And the influence factorsThis derivative competition with competitors all over the world. trend, not only from the design of adhesive process but also from the not only from the design of adhesive process but alsotofrom the which is inducing the development from macro micro service environment [4]. In this field, the main task is to service environment [4]. In this field, the main task is to markets, results in diminished lot sizes due to augmenting enhance the adhesive quality through process design before enhancevarieties the adhesive quality through process design before product (high-volume low-volume production) [1]. application. Many researches to have been conducted to study application. researches variety have been conducted study To cope with Many this augmenting as well as to betoable to identify possible optimization potentials in the existing 2212-8271 ©system, 2018 The Authors. Published Elsevier B.V. knowledge production is important toby a precise 2212-8271 © 2018 TheitAuthors. Published byhave Elsevier B.V.
process, geographer and structure of adhesive layer and joint process, geographer and structure of adhesive layer and joint in macro scale level, and surface texture in micro scale level in macro scale level, and surface texture in micro scale level [5, 6] on the adhesive quality. Several useful strategies have [5, 6] on the adhesive quality. Several useful strategies have been proposed to improve the shear strength of adhesive [7, 8]. been proposed to improve the shear strength of adhesive [7, 8]. By design of joint configuration, non-flat interface profile can Bythe design of joint configuration, non-flatmanufactured interface profile can of product range and characteristics and/or improve the adhesive quality that the shear strength of reverse improve the quality that the shear of reverse assembled in adhesive this system. In this context, the strength main challenge in bent joint and wavy-lap joint are higher than traditional flatbent joint and analysis wavy-lapisjoint are higher than traditional flatmodelling now not only to cope with single lap joint [9, 10]. Through pre-treatments on surfaces, the lap joint a[9, 10]. product Throughrange pre-treatments on surfaces, the products, limited or existing product surface roughness is thus increased and then the families, adhesive surface roughness isanalyze thus increased and then the to adhesive but also to be able to and to compare products define strength is enhanced [11, 12]. It is well known that the quality strength is enhanced [11,can 12]. is well known that theexisting quality new product families. beItobserved that classical of machined surface It has considerable influence on adhesiveof machined surface has considerable influence onoradhesiveproduct families are regrouped in function of clients features. sealing performance especially in micro-level the surface sealing performance especially infamilies micro-level the to surface However, product are hardly find. roughnessassembly and hasoriented not been systemically and sufficiently roughness and has not level, been products systemically and sufficiently On the product family differ mainly in two studied yet [13-15]. For engine manufacturing, the surfaces to studied yet [13-15]. For engine manufacturing, the and surfaces to main characteristics: (i) the number of components (ii) the be adhesively sealed are machined by milling operations and be adhesively sealed aremechanical, machined by milling electronical). operations and type of components (e.g. electrical, the surface qualities are determined by milling parameters [16]. theClassical surface qualities are determined by milling parameters [16]. methodologies considering mainly single products It thus makes a significance to analytically investigate the It thus makes a significance to analytically investigate the or solitary, already existing product families analyze effects of machined surface qualities on the reliabilitythe of effects structure of machined surface level qualities on the reliability of product on a physical (components level) which causes difficulties regarding an efficient definition and comparison of different product families. Addressing this
Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). 2212-8271 © 2018 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/) 2212-8271 © 2017 The Authors. Published by Elsevier B.V. Selection and peer-review under responsibility of the scientific committee of the 4th CIRP Conference on Surface Integrity (CSI 2018). Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 10.1016/j.procir.2018.05.011
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adhesive joint surface, and then provides a theoretical support for the design of face machining process. In this paper, three experiments have been conducted to study the adhesive-sealing performance related with milling parameters and also the machined surface topography induced by face milling. Through these experiments, the relationships of surface roughness parameters, milling parameters and functional sealing performance are modeled by response surface methodology (RSM). And then the effects of the machined surface topography and milling parameters on the reliability of the adhesive joint surface are systemically investigated. At last, optimized surface qualities and milling strategies are obtained based on these models with genetic algorithm (GA). Thus it provides a systemically thinking to improve the performance of adhesive bonding and sealing for its industry implementation; and also a theoretical support for the selection of face topographers and parameters’ optimization of face milling operation.
D6.0mm×12mm×57mm, side rake angle 15.0deg and back rake angle 30.0deg. The grade type is carbide PVD. The specimen to be machined was mounted on a case aluminum fixture. The material of specimen is aluminum alloy 5054 and the dimensions of blank are 90mm×25mm×10mm. The area to be machined is 35mm×25mm at one head of the specimen which is designed as the adhesive contact surface.
Nomenclature
f ap
Spindle speed Feed rate The depth of cut
ae
The radial depth of cut
s P
Shear strength Tensile load Joint overlap area
v
As Ra Sa Y x b Ypre Ymes
2D surface roughness 3D surface roughness Desired response Independent input variable Unknown coefficient Predicted values Experimental measured results
2. Experimental setup and procedure In this section, three experiments have been conducted: (1) The parameters of face milling operations for aluminum alloy was selected and then applied on a high speed machining center to machined samples’ surfaces with different qualities for the adhesive analysis; (2) The 3D texture of machined surfaces were measured with 3D surface optical profile and represented by cloud points, then the surface parameters were analyzed based on these cloud points; (3) The adhesive performance test was taken through tensile test. 2.1. Face milling experimental design Fig. 1 shows the overview of the face milling experimental setup. Face milling was implemented to produce specimens with different machined surfaces on a machining center (DMG HSC-75). The helical milling tool with two teeth (ID S4520660E2CZ2.0-HEMI, SECO) was installed for end milling process. The geography dimensions of milling tool are
Fig. 1. Overview of face milling experimental setup: (a) overview; (b) milling tool; (c) fixture and specimen.
All the milling operations were performed in two stages. Firstly, a rough milling process was performed before the finish milling in order to reduce the effect of initial rough surface on surface quality. The rough milling process for each specimen was conducted with coolant. The milling parameters, such as the radial depth of cut, the feed rate, and spindle speed, were chosen suitably to have a good surface quality and high machining efficiency simultaneously. The total depth of cut of each operation was adjusted according to the corresponding cutting depth of finish milling to maintain that after the finish milling the residual height of each specimen at the machining head maintains the same. Then the finish milling process was performed under dry conditions. Each specimen had different finishing milling parameters thus to produce different surfaces. Table 1. Parameters of face milling operations and its levels. Parameters
Level 1
Level 2
Level 3
Level 4
A
Spindle speed (r/min)
6000
8000
10000
12000
B
Feed rate (mm/min)
600
1200
2400
3600
C
The depth of cut (mm)
0.1
0.1
0.1
0.1
D
Radial depth of cut (mm)
2.6
3.0
3.6
4.0
Orthogonal array was used for the experimental design of finish milling in order to study how the face milling parameters affect the output factors and also to obtain optimized milling parameters and surface qualities to achieve higher adhesive sealing quality. As shown in Table 1, the face milling parameters concluded the spindle speed, feed rate, the radial depth of cut and the depth of cut. And each of them had four levels except the depth of cut which was set with the same value to guarantee the same residual height of machined specimen. The experimental design adopted Taguchi L16(4^3)
Shun Liu et al. / Procedia CIRP 71 (2018) 279–284 Shun Liu et al. / Procedia CIRP 00 (2018) 000–000
orthogonal method which consisted of 16 experiments with different milling parameters. Each experiment conducted twice to produce a pair of specimens with same surfaces in order to fabricate a single-lap joint for tensile test. 2.2. Surface measurement setup 3D surface optical profiler (KEYENCE KS-1100) and stereomicroscope (ZEISS Stemi-2000) were implemented to obtain the topographies of machined specimens as shown in Fig. 2. Laser head (LK-G10) was implemented on 3D surface optical profiler to capture the point cloud of machined surface area. The measurement area was 7000μm×5000μm with a scanning step of 5μm on a scanning speed 5000μm/s. Then topography of each specimen was demonstrated by a point cloud with 1001×1401 matrix. With the point cloud matrix, 3D surface parameters can be obtained software MATLAB R2014a. All the specimens were also measured with stereomicroscope with ×40 magnification in order to obtain the micro textures. The 2D surface roughness was measured with a surface roughness tester (Mitutoyo SJ-210).
281 3
To prepare the test samples, a couple of machined specimens milled by the same parameters in face milling experiments were boned together as a single lap joint sample. All the sixteen samples were bonded at the same time. The sealing adhesive applied is the room temperature vulcanized (RTV) silicone rubber (Loctite5970). After the fabrication procedures, test samples were stored in controlled chamber at temperature of 23℃ and relative humidity of 50%RH, and then they were tested at same curing time of 21 days. All the samples were prepared with same procedure at the same time. All the obtained samples were tested using Zwick/Roell Z010 testing machine at room temperature for the determination of joint shear strength. The displacement rate was constant (10mm/min). The shear strength is evaluated by:
s
P As
(1)
2.4. Experimental procedure Input face milling variables Spindle speed
Feed rate
Depth of cut
Radial depth of cut
Face Milling Experiment B
A Surface Measurement Experiment
(a) (b) Fig. 2. Measurement setup of: (a) optical profiler; (b) stereomicroscope.
2.3. Tensile test
2D surface roughness
Single Lap Joint Tensile Test
3D surface roughness
Shear strength
Output measurement variables Fig. 4. Experimental design.
To evidence the effect of the surface qualities on the toughening properties of the adhesives between two machined surfaces, the single lap joint test was carried out to determinate the joint shear strength (or saying the failure load because all the samples were test with a same joint overlap area). In Fig. 3 the structure of single-lap joint, the sizes of adherent and adhesive were defined considering the geometries of the machined samples and the working conditions of adhesive sealing between two machined parts. The thickness of the adhesive was chosen as constant 0.1mm to make sure the failure type was not substrate failure. 9mm
10mm
RTV, 0.1mm
25mm 30mm
Fig. 3. Structure of single lap joint sample.
In this study, all the specimens are machined by face milling experiment with different parameters determined by orthogonal experiment method. The four experimental variables are the spindle speed, feed rate, depth of cut and radial depth of cut. Each variable had four levels and totally sixteen pairs of specimens were produced in the experiment. Then the surfaces were measured and surface quality indicators were calculated. The typical parameters used in this study are 2D/3D surface roughness derived from the direct outcome measures. A total of sixteen combinations were tested in single lap joint tensile test to obtain the shear strength. The procedures of experimental design are shown in Fig. 4. After the experiments, the relationship between input parameters (face milling variables) and output variables (surface quality parameters and tensile test outcomes) can be modeled by empirical modeling approach of RSM. In Fig. 4, Leg A and Leg B highlight the two response functions. Then the established response functions (shown as Leg A) can be used to maximum the shear strength of functional performance by determining the approximation face milling parameters using GA. If the optimized face milling parameters are
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obtained, then with the response functions shown in Leg B, surface parameters of machined surface that have strong shear strength and perfect adhesive performance can be determined. 3. Experimental results and modeling using RSM 3.1. Experimental results Sixteen pairs of specimens were successfully machined by face milling as shown in Fig. 5. From the figure, it is seen that all the surfaces have different textures in macroscopic scale view. It suggested that face milling with different parameters are capable of producing surfaces with different qualities.
2D/3D surface roughness are [0.365, 1.668] and [1.838, 4.337], respectively. The experimental results of shear strength are shown in Fig. 8. And the relationship between shear strength and surface parameters of 2D/3D surface roughness are shown in Fig. 7. It can be seen that the relationship between shear strength and surface parameters are quite complicated. The nonlinear relations donate that the shear strength (adhesive performance) differs with surface topography quality and cannot be determined directly by single surface parameter. And the lower or higher surface roughness does not mean higher shear strength. Thus it is be of great importance to build a model to describe the relationship between shear strength and surface qualities through a bridge such as face milling parameters which can connect the two output variables together.
(a) Fig. 5. Sixteen pairs of specimens with machined surfaces by milling.
(b)
Fig. 7. The relationship between shear strength and surface parameters of: (a) 2D surface roughness; (b) 3D surface roughness
3.2. Regression analysis using RSM
7mm×5mm
1000μm
In this study, the collection of experimental data adopts a standard RSM design and the desired responses and independent input variables are represented by a quadratic regression model with a general second-order polynomial regression formula given in Eq. (2). k
(a)
k
k
Y f X b0 bi xi bij xi x j i 1
(2)
i 1 j i
bi , bij Where Y is the desired response, Y [ s , Ra , Sa ]t ; b0 ， are the unknown coefficients, each of them is a 3×1 vector corresponding to the desired response; xi and x j are the 7mm×5mm
1000μm
(b) Fig. 6. Surface topography measured by 3D surface optical profiler in sampling area 7mm×5mm and stereomicroscope (40×) with different surface roughness in experiment: (a) No. 3, Ra 1.571μm; (b) No. 14, Ra 0.65μm.
independent input variables, X [v, f , ae , a p ] . The shear strength model is given by Eq. (3).
s 0.633+104 v 8.8 104 f 1.130ae 108 vf 4.87 105 vae +6.30 105 fae 9
2
8
2
2 e
(3)
5.94 10 v 3.0 10 f 0.10a The measurement results and surface parameters obtained are shown in Fig. 6, Fig. 9 and Fig. 10. Fig. 6 shows the The quadratic model of 2D surface roughness can be typical surface topographies with different surface qualities by written as different milling operations. It is observed that the surface texture has more obvious Ra 2.3932 104 v 1.43 103 f 4.95 102 ae grooves and peaks with larger surface roughness (Ra 1.571μm) 108 vf 7.66 105 vae 1.16 104 fae (4) in Fig. 6 (a) when compared to that of the texture with lower 8 2 7 2 2 2 surface roughness (Ra 0.65μm) in Fig. 6 (b). Surface 10 v 1.25 10 f 9.34 10 ae parameters are obtained by post-process of point cloud with MATLAB. From the table, as it can be seen that the range of The 3D surface roughness model is given below in Eq. (5).
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Sa 3.306 103 v 3.19 103 f 5.344ae 108 vf 1.48 104 vae 4.73 104 fae 8
2
7
2
(5)
2 e
10 v 2.85 10 f 0.827a
The predicted values obtained from the quadratic models were compared with experimental measured results. The percentage error is calculated by:
Ypre Ymes Error % 100 Ymes
(6)
The relationship between the predicted and measured results was shown in Fig. 8, Fig. 9, and Fig. 10. Fig. 10. Comparison between predicted and measured values for 3D surface roughness.
4. Optimization and discussion
Fig. 8. Comparison between predicted and measured values for shear strength.
In this study, the main work concerned with maximization of shear strength for improving adhesive functional performance was subjected to design parameters of face milling. And then with the optimized face milling parameters, the desired quality parameters of machined surface was derived and used for the monitoring of machined surface quality variation in manufacturing. Then the optimization model for face milling parameters under special requirements, which concludes the objective function for shear strength and the constraint conditions of 2D/3D surface roughness for surface qualities and limitation ranges of optimal variables, is expressed as following
maximize s =f X t lb Ra , Sa ub s t . . X X X
(7)
Where, lb and ub are the lower and upper boundary of surface quality requirements, lb= 0.3,1.5 and ub= 2.0,5.0 , t
t
respectively. X and X are the lower and upper range limitation
of
X =5000, 400,0.1, 2.4
t
variables,
and
X 13000, 4000,0.1, 4.2 , respectively. t
Fig. 9. Comparison between predicted and measured values for 2D surface roughness.
The errors between predicted and measured results of 2D/3D surface roughness are within an acceptable range. While the relative errors of shear strength are a bit large especially in sixth and eleventh experiment. The larger gap comes from the error and complexity of tensile experiment, but all the percentage errors are acceptable while excluding sixth and eleventh experiment. Thus the established quadratic regression models are validated for the prediction of response variables with the input face milling parameters.
The constrained optimization problem was solved by GA. The GA is programmed with MATLAB R2014a for optimizing the face milling parameters. The relationship between fitness values and evolutionary generations is shown in Fig. 11. It shows that the optimization process convergences after 87th generation which shows the fast rate of convergence and excellent fitness searching capability of the applied algorithm. After the optimization run, the optimized shear strength is represented as 2.124 N/mm2 and the corresponding optimized
values of design parameters are X=13000, 4000,0.1,3.748 . t
Then the results of desired quality parameters of machined surface are calculated with Eq. (4) and Eq. (5), Ra =1.226 and
Sa 3.242 , respectively. It indicates that the largest shear
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strength does not appear with the largest or smallest surface roughness but in a suitable roughness range of surface quality.
Acknowledgements This research was supported from the National Natural Science Foundation of China under the key project No.51535007, the National Natural Science Foundation of China under the project No.51675339, and Shanghai GM PTME company under the project No. PS23005233. References
Fig. 11. The fitness and mean values of objective variable during optimization process change along with evolutionary generations.
5. Conclusions This study investigated the effects of the spindle speed, feed rate, depth of cut and radial depth of cut on surface quality parameters (2D/3D surface roughness) and adhesive functional performance (shear strength) using orthogonal experimental design method. The corresponding models were established with RSM and then responses were optimized using GA. The results obtained in the three experimental studies, including face milling experiment, surface measurement experiment and tensile test, were used to establish quadratic models. It was found that the two models can be used for the prediction of 2D/3D surface roughness and tensile strength with acceptable errors. It found that the shear strength is nonlinear with single surface parameters such as 2D/3D surface roughness, and it is reasonable to connect these two variables through face milling parameters. The optimal values for maximum tensile shear strength are the spindle speed of 13000r/min, feed rate of 4000mm/min, and radial depth of cut of 3.748mm. And the 2D/3D surface roughness values subject to the optimal face milling parameters are 1.226μm and 3.242μm, respectively. Thus they can be used to monitor the quality variation of manufacturing. The proposed methodology in this study is useful to enhance adhesive functional performance by optimization design of face milling parameters and surface parameters. It can be easily employed in real applications without computational complexity.
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