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Research Papers

A Coupled Model for the Prediction of Surface Variation in Face Milling Large-Scale Workpiece With Complex Geometry

[+] Author and Article Information
Shun Liu, Sun Jin

State Key Laboratory of
Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China;
Shanghai Key Laboratory of Digital Manufacture
for Thin-Walled Structures,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

Xue-Ping Zhang

State Key Laboratory of
Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China
e-mail: zhangxp@sjtu.edu.cn

Kun Chen, Ang Tian

State Key Laboratory of
Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China;
Shanghai Key Laboratory of
Digital Manufacture for Thin-Walled Structures,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

Li-Feng Xi

State Key Laboratory of
Mechanical System and Vibration,
School of Mechanical Engineering,
Shanghai Jiao Tong University,
Shanghai 200240, China

1Corresponding author.

Manuscript received June 29, 2018; final manuscript received November 28, 2018; published online January 22, 2019. Assoc. Editor: Guillaume Fromentin.

J. Manuf. Sci. Eng 141(3), 031009 (Jan 22, 2019) (14 pages) Paper No: MANU-18-1499; doi: 10.1115/1.4042188 History: Received June 29, 2018; Revised November 28, 2018

Face milling commonly generates surface quality of variation, is especially severe for milling of large-scale components with complex surface geometry such as cylinder block, engine head, and valve body. Thus surface variation serves as an important indicator both for machining parameter selection and components' service performance such as sealing, energy consumption, and emission. An efficient and comprehensive numerical model is highly desired for the prediction of surface variation of entire surface. This study proposes a coupled numerical simulation method, updating finite element (FE) model iteratively based on integration of data from abaqus and matlab, to predict surface variation induced by face milling of large-scale components with complex surfaces. Using the coupled model, three-dimensional (3D) variation of large-scale surface can be successfully simulated by considering face milling process including dynamic milling force, spiral curve of milling trajectory, and intermittently rotating contact characteristics. Surface variation is finally represented with point cloud from iterative FE analysis and verified by face milling experiment. Comparison between measured and predicted results shows that the new prediction method can simulate surface variation of complex components well. Based on the verified model, a set of analyses are conducted to evaluate the effects of local stiffness nonhomogenization and milling force variation on machined surface variation. It demonstrates that surface variation with surface peaks and concaves is strongly correlated with local stiffness nonhomogenization especially in feed direction. And thus the coupled prediction method provides a theoretical and efficient way to study surface variation induced by face milling of large-scale complex components.

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References

Liao, Y. G. , and Hu, S. J. , 2001, “An Integrated Model of a Fixture-Workpiece System for Surface Quality Prediction,” Int. J. Adv. Manuf. Technol., 17(11), pp. 810–818. [CrossRef]
Carvalho, H. M. B. D. , Gomes, J. D. O. , Schmidt, M. A. , and Brandão, V. L. C. , 2015, “Vibration Analysis and Energy Efficiency in Interrupted Face Milling Processes,” Procedia CIRP, 29, pp. 245–250. [CrossRef]
Xie, J. Q. , Agapiou, J. S. , Stephenson, D. A. , and Hilber, P. , 2003, “Machining Quality Analysis of an Engine Cylinder Head Using Finite Element Methods,” J. Manuf. Processes, 5(2), pp. 170–184. [CrossRef]
Du, S. , Liu, C. , and Huang, D. , 2015, “A Shearlet-Based Separation Method of 3D Engineering Surface Using High Definition Metrology,” Precis. Eng., 40, pp. 55–73. [CrossRef]
Gu, F. , Melkote, S. N. , Kapoor, S. G. , and Devor, R. E. , 1997, “A Model for the Prediction of Surface Flatness in Face Milling,” ASME J. Manuf. Sci. Eng., 119(4), pp. 476–484. [CrossRef]
Tai, B. L. , Stephenson, D. A. , and Shih, A. J. , 2011, “Improvement of Surface Flatness in Face Milling Based on 3-D Holographic Laser Metrology,” Int. J. Mach. Tools Manuf., 51(6), pp. 483–490. [CrossRef]
Liu, S. , Jin, S. , Zhang, X. , Wang, L. , Mei, B. , and Hu, B. , 2017, “Controlling Topography of Machined Surface for Adhesive-Sealing,” ASME Paper No. MSEC2017-2674.
Rao, R. V. , 2013, Advanced Modeling and Optimization of Manufacturing Processes, Springer, Berlin, pp. 317–338.
Benardos, P. G. , and Vosniakos, G. C. , 2003, “Predicting Surface Roughness in Machining: A Review,” Int. J. Mach. Tools Manuf., 43(8), pp. 833–844. [CrossRef]
Shi, Z. Y. , Liu, L. N. , and Liu, Z. Q. , 2015, “Influence of Dynamic Effects on Surface Roughness for Face Milling Process,” Int. J. Adv. Manuf. Technol., 80(9–12), pp. 1823–1831.
Hessainia, Z. , Belbah, A. , Yallese, M. A. , Mabrouki, T. , and Rigal, J. , 2013, “On the Prediction of Surface Roughness in the Hard Turning Based on Cutting Parameters and Tool Vibrations,” Measurement, 46(5), pp. 1671–1681. [CrossRef]
Felhő, C. , Karpuschewski, B. , and Kundrák, J. , 2015, “Surface Roughness Modelling in Face Milling,” Procedia CIRP, 31, pp. 136–141. [CrossRef]
Wang, M. , Du, S. , and Xi, L. , 2015, “Predicting Machined Surface Topography Based on High Definition Metrology,” IFAC-PapersOnLine, 48(3), pp. 1013–1017. [CrossRef]
Tai, B. L. , Stephenson, D. A. , and Shih, A. J. , 2009, “Improvement of Surface Flatness in Face Milling by Varying the Tool Cutting Depth and Feed Rate,” ASME Paper No. MSEC2009-84208.
Suriano, S. , Wang, H. , Shao, C. , Hu, S. J. , and Sekhar, P. , 2015, “Progressive Measurement and Monitoring for Multi-Resolution Data in Surface Manufacturing Considering Spatial and Cross Correlations,” IIE Trans., 47(10), pp. 1033–1052.
Hai, T. N. , Wang, H. , and Hu, S. J. , 2013, “Characterization of Cutting Force Induced Surface Shape Variation in Face Milling Using High-Definition Metrology,” ASME J. Manuf. Sci. Eng., 135(4), p. 041014.
Nguyen, H. T. , Wang, H. , and Hu, S. J. , 2014, “Modeling Cutter Tilt and Cutter-Spindle Stiffness for Machine Condition Monitoring in Face Milling Using High-Definition Surface Metrology,” Int. J. Adv. Manuf. Technol., 70(5–8), pp. 1323–1335. [CrossRef]
Tai, B. L. , Wang, H. , Hai, N. , Hu, S. J. , and Shih, A. , 2012, “Surface Variation Reduction for Face Milling Based on High-Definition Metrology,” ASME Paper No. MSEC2012-7208.
Hai, T. N. , Wang, H. , Tai, B. L. , Ren, J. , Hu, S. J. , and Shih, A. , 2016, “High-Definition Metrology Enabled Surface Variation Control by Cutting Load Balancing,” ASME J. Manuf. Sci. Eng., 138(2), p. 021010.
Rai, J. K. , and Xirouchakis, P. , 2008, “Finite Element Method Based Machining Simulation Environment for Analyzing Part Errors Induced During Milling of Thin-Walled Components,” Int. J. Mach. Tools Manuf., 48(6), pp. 629–643. [CrossRef]
Sundararaman, K. A. , Guharaja, S. , Padmanaban, K. P. , and Sabareeswaran, M. , 2014, “Design and Optimization of Machining Fixture Layout for End-Milling Operation,” Int. J. Adv. Manuf. Technol., 73(5–8), pp. 669–679. [CrossRef]
Wan, M. , Zhang, W. H. , Qiu, K. P. , Gao, T. , and Yang, Y. H. , 2005, “Numerical Prediction of Static Form Errors in Peripheral Milling of Thin-Walled Workpieces With Irregular Meshes,” ASME J. Manuf. Sci. Eng., 127(1), pp. 13–22. [CrossRef]
Kang, Y. , and Wang, Z. , 2013, “Two Efficient Iterative Algorithms for Error Prediction in Peripheral Milling of Thin-Walled Workpieces Considering the In-Cutting Chip,” Int. J. Mach. Tools Manuf., 73, pp. 55–61. [CrossRef]
Dong, Z. , Jiao, L. , Wang, X. , Liang, Z. , Liu, Z. , and Yi, J. , 2016, “FEA-Based Prediction of Machined Surface Errors for Dynamic Fixture-Workpiece System During Milling Process,” Int. J. Adv. Manuf. Technol., 85(1–4), pp. 299–315. [CrossRef]
Liu, E. A. , and Zou, Q. , 2011, “Machined Surface Error Analysis—A Face Milling Approach,” J. Adv. Manuf. Syst., 10(2), pp. 293–307. [CrossRef]
Mathieu, F. , Leclerc, H. , Hild, F. , and Roux, S. , 2015, “Estimation of Elastoplastic Parameters Via Weighted FEMU and Integrated-DIC,” Exp. Mech., 55(1), pp. 105–119. [CrossRef]
Lin, J. , Jin, S. , Zheng, C. , Li, Z. , and Liu, Y. , 2014, “Compliant Assembly Variation Analysis of Aeronautical Panels Using Unified Substructures With Consideration of Identical Parts,” Comput.-Aided Des., 57, pp. 29–40. [CrossRef]
Heikkala, J. , 1995, “Determining of Cutting-Force Components in Face Milling,” J. Mater. Process. Technol., 52(1), pp. 1–8. [CrossRef]

Figures

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Fig. 3

Three coordinate systems for milling operation

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Fig. 2

The process of modeling surface variation by face milling

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Fig. 1

The framework of the new coupled FE model to simulate surface variation

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Fig. 8

Definition of machined surface variation

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Fig. 4

Typical cutting trajectory of face milling with five-tooth cutter

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Fig. 5

Different insert engaged milling areas in workpiece

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Fig. 6

Geometric description of immersion point between workpiece and cutter

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Fig. 7

Three force components in face milling

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Fig. 9

Simulation algorithm to determine surface variation

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Fig. 10

Dimension of the aluminum workpiece (mm)

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Fig. 14

Experimental setup of face milling operation

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Fig. 15

Workpiece measurement with CMM on (a) sampling point cloud and (b) sampling lines

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Fig. 16

The statistical comparison between measured and simulation results of point cloud

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Fig. 17

Comparison of experimental and simulated results on sampling lines: (a) A1–A1, (b) A2–A2, (c) B1–B1, (d) B2–B2, (e) B3–B3, and (f) B4–B4

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Fig. 11

Finite element model of workpiece with irregular mesh

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Fig. 12

Stress distributions during FE analysis at typical steps: (a) first step, (b) step 183, (c) step 246, (d) step 461, (e) step 656, and (f) last step

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Fig. 13

Final result of overall machined surface variation: (a) an image of point cloud and (b) the distribution of error on each point

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Fig. 18

Point cloud of Z-milling force at each milling position

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Fig. 19

Correlation analysis of normalized milling force and machined surface variation on surface nodes plotted in an ascending order of (a) node's number and (b) original results of residual surface height values

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Fig. 20

Analyses of local stiffness: (a) point cloud of machined surface variation induced by uniform milling force, (b) its correlation with original simulated machined surface variation plotted in an ascending order of node's number, and (c) correlation plotted in an ascending order of residual surface height values of original results

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Fig. 21

Correlation analyses of machined surface errors on the sampling lines: in feed direction (a) Y = −32 and (b) Y = +32; and in cross sections (c) X = 5, (d) X = 65, (e) X = 125, and (f) X = 185

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