Simulation model optimization for bonnet polishing considering consistent contact area response

doi:10.1007/s11465-024-0799-x

Front. Mech. Eng.

2024, Vol. 19

Issue (4) : 27 https://doi.org/10.1007/s11465-024-0799-x

Simulation model optimization for bonnet polishing considering consistent contact area response

Yanjun HAN¹(

), Haiyang ZHANG¹, Menghuan YU¹, Jinzhou YANG²(

), Linmao QIAN¹

¹. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
². Institute of Data Science, Maastricht University, Maastricht 6229EN, the Netherlands

Download: PDF(8511 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Simulation model optimization plays a crucial role in the accurate prediction of material removal function in bonnet polishing processes, but model complexity often poses challenges to the practical implementation and efficiency of these processes. This paper presents an innovative method for optimizing simulation model parameters, focusing on achieving consistent contact area and the accurate prediction of the material removal function while preventing increase in model complexity. First, controllable and uncontrollable factors in bonnet simulations are analyzed, and then a simplified contact model is developed and applied under constant force conditions. To characterize the bonnet’s contact performance, a contact area response curve is introduced, which can be obtained through a series of single spot contact experiments. Furthermore, a rubber hyperelastic parameter optimization model based on a neural network is proposed to achieve optimal matching of the contact area between simulation and experiment. The average deviation of the contact area under different conditions was reduced from 22.78% before optimization to 3.43% after optimization, preliminarily proving the effectiveness of the proposed simulation optimization model. Additionally, orthogonal experiments are further conducted to validate the proposed approach. The comparison between the experimental and predicted material removal functions reveals a high consistency, validating the accuracy and effectiveness of the proposed optimization method based on consistent contact response. This research provides valuable insights into enhancing the reliability and effectiveness of bonnet polishing simulations with a simple and practical approach while mitigating the complexity of the model.

Keywords bonnet polishing simulation contact area tool influence function optimization

Corresponding Author(s): Yanjun HAN,Jinzhou YANG

About author:

^#These authors contributed equally to this work.

Issue Date: 13 August 2024

Cite this article:

Yanjun HAN,Haiyang ZHANG,Menghuan YU, et al. Simulation model optimization for bonnet polishing considering consistent contact area response[J]. Front. Mech. Eng., 2024, 19(4): 27.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-024-0799-x
https://academic.hep.com.cn/fme/EN/Y2024/V19/I4/27

Fig.1 Controllable and uncontrollable factors in the modeling of bonnet polishing.

Fig.2 Robotic bonnet polishing platform. (a) Robotic polishing system, (b) pad dressing system, (c) bonnet profile measurement system, and (d) pad profile comparison before and after dressing.

Fig.3 Schematic of the bonnet tool feed motion along the (a) tool axis direction and (b) workpiece normal direction.

Tab.1 Key material property parameters for bonnet polishing

Fig.4 Determination of rubber hyperelastic parameters. (a) Electronic universal material testing machine, (b) uniaxial tension specimen dimensions, (c) uniaxial tension testing, and (d) uniaxial compression testing.

Fig.5 Uniaxial tension and compression stress–strain diagram.

Fig.6 Characterization method of the contact area between the bonnet tool and workpiece in the (a) experiments and (b) simulations.

Fig.7 Bonnet contact modeling: (a) physical structure of bonnet tool and (b) corresponding finite element model.

Tab.2 Mesh parameters for each component in the simulation model

Fig.8 Influence of mesh size on simulation results. (a) Pressure distribution and (b) contact area variation for a 5 N contact force and the total calculation time for four operating conditions (continuous loading at 5, 10, 15, and 20 N) under different mesh sizes.

Fig.9 Displacement curve of the lowest point on the bonnet as the load is applied.

Fig.10 Experimental and simulation results before optimization under different contact forces. (a) Comparison of contact area; (b) contact area deviation.

Fig.11 Calculation of the linear velocity of each point in the contact area. (a) Schematic diagram of geometric solution; (b) distribution of linear velocity in the contact area.

Fig.12 Flowchart of proposed simulation optimization model.

Fig.13 Deviation between the predicted results of forward data set and the simulation results.

Fig.14 Simulation optimization model parameter analysis results. (a) Fitting results of different curve fitting equations and (b) root mean square error (RMSE) corresponding to different curve fitting equations and initial data set sizes.

Fig.15 Experimental and simulation results after optimization under different contact forces. (a) Comparison of contact area; (b) contact area deviation.

Fig.16 Comparison of simulated pressure distribution before and after model optimization. (a) 3D pressure distribution; (b) pressure curve along x-axis cross-section.

Tab.3 Control factors and levels in orthogonal experiments

Tab.4 L16(4⁵) orthogonal table

Fig.17 Comparison of material removal functions between experiment and simulation data in the presence of orthogonal parameters. (a) 3D morphology, (b) x-axis section, (c) y-axis section, and (d) material removal rate (MRR).

Fig.18 Contribution percentages of each factor by analysis of variance approach.

1	X P Huang, Z Z Wang, Z W Lin. Movement modeling and control for robotic bonnet polishing. Chinese Journal of Mechanical Engineering, 2022, 35(1): 68 https://doi.org/10.1186/s10033-022-00751-y
2	Z W Wu, J Y Shen, Y F Peng, X Wu. Review on ultra-precision bonnet polishing technology. The International Journal of Advanced Manufacturing Technology, 2022, 121(5–6): 2901–2921 https://doi.org/10.1007/s00170-022-09501-9
3	X L Ke, Y H Yu, K S Li, T Y Wang, B Zhong, Z Z Wang, L B Kong, J Guo, L Huang, M Idir, C Liu, C J Wang. Review on robot-assisted polishing: status and future trends. Robotics and Computer-Integrated Manufacturing, 2023, 80: 102482 https://doi.org/10.1016/j.rcim.2022.102482
4	C J Wang, W Yang, Z Z Wang, X Yang, Z J Sun, B Zhong, R Pan, P Yang, Y B Guo, Q Xu. Highly efficient deterministic polishing using a semirigid bonnet. Optical Engineering, 2014, 53(9): 095102 https://doi.org/10.1117/1.OE.53.9.095102
5	R Pan, C F Hu, J W Fan, Z Z Wang, X P Huang, F Lu. Study on optimization of the dynamic performance of the robot bonnet polishing system. The International Journal of Advanced Manufacturing Technology, 2023, 125(9–10): 4561–4577 https://doi.org/10.1007/s00170-023-10998-x
6	R Pan, W Y Zhao, Z Z Wang, S T Ji, X S Gao, D J Chen, J W Fan. Research on an evaluation model for the working stiffness of a robot-assisted bonnet polishing system. Journal of Manufacturing Processes, 2021, 65: 134–143 https://doi.org/10.1016/j.jmapro.2021.03.013
7	R Pan, X X Zhu, Z Z Wang, W Y Zhao, K Sun, D J Chen, J W Fan. Optimization of static performance for robot polishing system based on work stiffness evaluation. Proceedings of the Institution of Mechanical Engineers. Part B, Journal of Engineering Manufacture, 2023, 237(4): 519–531 https://doi.org/10.1177/09544054221105151
8	L Qiu. Key technology research on flexible polishing of bonnet for robot of aluminum alloy. Thesis for the Master’s Degree. Xiamen: Xiamen University of Technology, 2021 (in Chinese)
9	W L Zhu, A Beaucamp. Compliant grinding and polishing: a review. International Journal of Machine Tools & Manufacture, 2020, 158: 103634 https://doi.org/10.1016/j.ijmachtools.2020.103634
10	J F Song. Optimizing process parameters and related technologies for bonnet polishing of curved surface optical parts. Dissertation for the Doctoral Degree. Harbin: Harbin Institute of Technology, 2009 (in Chinese)
11	C Wang, Y J Han, H Y Zhang, C L Liu, L Jiang, L M Qian. Suppression of mid-spatial-frequency waviness by a universal random tree-shaped path in robotic bonnet polishing. Optics Express, 2022, 30(16): 29216–29233 https://doi.org/10.1364/OE.468103
12	Z C Cao, C F Cheung. Multi-scale modeling and simulation of material removal characteristics in computer-controlled bonnet polishing. International Journal of Mechanical Sciences, 2016, 106: 147–156 https://doi.org/10.1016/j.ijmecsci.2015.12.011
13	C C Shi, Y F Peng, L Hou, Z Z Wang, Y B Guo. Improved analysis model for material removal mechanisms of bonnet polishing incorporating the pad wear effect. Applied Optics, 2018, 57(25): 7172–7186 https://doi.org/10.1364/AO.57.007172
14	B Zhong, X H Chen, R Pan, J Wang, H Z Huang, W H Deng, Z Z Wang, R Q Xie, D F Liao. The effect of tool wear on the removal characteristics in high-efficiency bonnet polishing. The International Journal of Advanced Manufacturing Technology, 2017, 91(9–12): 3653–3662 https://doi.org/10.1007/s00170-017-0015-9
15	B Zhong, H Z Huang, X H Chen, J Wang, R Pan, Z J Wen. Impact of pad conditioning on the bonnet polishing process. The International Journal of Advanced Manufacturing Technology, 2018, 98(1–4): 539–549 https://doi.org/10.1007/s00170-018-2250-0
16	W L Zhu, B Anthony. Investigation of critical material removal transitions in compliant machining of brittle ceramics. Materials & Design, 2020, 185: 108258 https://doi.org/10.1016/j.matdes.2019.108258
17	J B Feng, Y F Zhang, M Q Rao, Y Y Zhao, Y H Yin. An adaptive bonnet polishing approach based on dual-mode contact depth TIF. The International Journal of Advanced Manufacturing Technology, 2023, 125(5–6): 2183–2194 https://doi.org/10.1007/s00170-022-10694-2
18	W L Zhu, O Pakenham-Walsh, K Copson, P Charlton, K Tatsumi, B F Ju, A Beaucamp. Mechanism of mid-spatial-frequency waviness removal by viscoelastic polishing tool. CIRP Annals, 2022, 71(1): 269–272 https://doi.org/10.1016/j.cirp.2022.04.056
19	C J Wang, W Yang, Y B Guo, Z Z Wang, M J Zheng. Research on parameters of bonnet polishing based on FEA. Advanced Materials Research, 2012, 403–408: 486–490
20	C Fan, K X Liu, Y G Chen, Y C Xue, J Zhao, A Khudoley. A new modelling method of material removal profile for electrorheological polishing with a mini annular integrated electrode. Journal of Materials Processing Technology, 2022, 305: 117589 https://doi.org/10.1016/j.jmatprotec.2022.117589
21	C Fan, X F Wang, K X Liu, Y G Chen, F S Liang, Z Wang, J Zhao. Material removal mechanism in and experiments of electrorheological polishing of foldable intraocular lenses at low temperatures. Journal of Manufacturing Processes, 2023, 101: 1032–1045 https://doi.org/10.1016/j.jmapro.2023.06.047
22	W F Yao, B H Lyu, T Q Zhang, L G Guo, Y Song. Effect of elastohydrodynamic characteristics on surface roughness in cylindrical shear thickening polishing process. Wear, 2023, 530–531: 205026
23	W F Yao, Q Q Chu, B H Lyu, C W Wang, Q Shao, M Feng, Z Wu. Modeling of material removal based on multi-scale contact in cylindrical polishing. International Journal of Mechanical Sciences, 2022, 223: 107287 https://doi.org/10.1016/j.ijmecsci.2022.107287
24	H Y Li, D Walker, G Y Yu, W Zhang. Modeling and validation of polishing tool influence functions for manufacturing segments for an extremely large telescope. Applied Optics, 2013, 52(23): 5781–5787 https://doi.org/10.1364/AO.52.005781
25	D W Kim, S W Kim. Novel simulation technique for efficient fabrication of 2-m class hexagonal segments for extremely large telescope primary mirrors. In: Proceedings of SPIE Optical Design and Testing II. Beijing: SPIE, 2005, 48–59
26	D W Kim, S W Kim. Static tool influence function for fabrication simulation of hexagonal mirror segments for extremely large telescopes. Optics Express, 2005, 13(3): 910–917 https://doi.org/10.1364/OPEX.13.000910
27	C J Wang, Z Z Wang, X C Yang, Z J Sun, Y F Peng, Y B Guo, Q Xu. Modeling of the static tool influence function of bonnet polishing based on FEA. The International Journal of Advanced Manufacturing Technology, 2014, 74(1–4): 341–349 https://doi.org/10.1007/s00170-014-6004-3
28	J F Song, Y X Yao. Material removal model considering influence of curvature radius in bonnet polishing convex surface. Chinese Journal of Mechanical Engineering, 2015, 28(6): 1109–1116 https://doi.org/10.3901/CJME.2015.0923.114
29	J F Song, Y X Yao, Y G Dong, B Dong. Prediction of surface quality considering the influence of the curvature radius for polishing of a free-form surface based on local shapes. The International Journal of Advanced Manufacturing Technology, 2018, 95(1–4): 11–25 https://doi.org/10.1007/s00170-017-0934-5
30	B Zhong, C J Wang, X H Chen, J Wang. Time-varying tool influence function model of bonnet polishing for aspheric surfaces. Applied Optics, 2019, 58(4): 1101–1109 https://doi.org/10.1364/AO.58.001101
31	R Pan, B Zhong, D J Chen, Z Z Wang, J W Fan, C Y Zhang, S N Wei. Modification of tool influence function of bonnet polishing based on interfacial friction coefficient. International Journal of Machine Tools & Manufacture, 2018, 124: 43–52 https://doi.org/10.1016/j.ijmachtools.2017.09.003
32	R Pan, X X Zhu, Z Z Wang, D J Chen, S T Ji, J W Fan, R Wang. Modification of tool influence function for bonnet polishing tool based on analysis of interfacial contact state. Journal of Mechanical Science and Technology, 2022, 36(6): 2825–2836 https://doi.org/10.1007/s12206-022-0515-x
33	Z C Cao, C F Cheung, X Zhao. A theoretical and experimental investigation of material removal characteristics and surface generation in bonnet polishing. Wear, 2016, 360–361: 137–146
34	Z C Cao, C F Cheung, L T Ho, M Y Liu. Theoretical and experimental investigation of surface generation in swing precess bonnet polishing of complex three-dimensional structured surfaces. Precision Engineering, 2017, 50: 361–371 https://doi.org/10.1016/j.precisioneng.2017.06.010
35	C C Shi, Y F Peng, L Hou, Z Z Wang, Y B Guo. Micro-analysis model for material removal mechanisms of bonnet polishing. Applied Optics, 2018, 57(11): 2861–2872 https://doi.org/10.1364/AO.57.002861
36	S L Wan, X C Zhang, H Zhang, M Xu, X Q Jiang. Modeling and analysis of sub-aperture tool influence functions for polishing curved surfaces. Precision Engineering, 2018, 51: 415–425 https://doi.org/10.1016/j.precisioneng.2017.09.013
37	Z Zhao, Z W Dong, H B Wu. Effect of particle size on the ultraprecision polishing process of titanium alloy Ti6Al4V. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 2019, 233(13): 4490–4496 https://doi.org/10.1177/0954406219831036
38	X Zhang, H B Wu. Influence of path on the ultra-precision polishing process of titanium alloy Ti6Al4V. The International Journal of Advanced Manufacturing Technology, 2018, 98(5–8): 1155–1162 https://doi.org/10.1007/s00170-018-2315-0
39	W L Zhu, S Ben Achour, A Beaucamp. Centrifugal and hydroplaning phenomena in high-speed polishing. CIRP Annals, 2019, 68(1): 369–372 https://doi.org/10.1016/j.cirp.2019.04.018
40	R Pan, W Y Zhao, B Zhong, D J Chen, Z Z Wang, C Q Zha, J Q Fan. Evaluation of removal characteristics of bonnet polishing tool using polishing forces collected online. Journal of Manufacturing Processes, 2019, 47: 393–401 https://doi.org/10.1016/j.jmapro.2019.03.029
41	C C Shi, C J Wang, C F Cheung, Z L Zhang, Z Li, L T Ho, W J Deng, X J Zhang. Curvature effect-based modeling and experimentation of the material removal in polishing optical surfaces using a flexible ball-end tool. Optics Express, 2022, 30(14): 24611–24638 https://doi.org/10.1364/OE.460327

[1]	Zhongyu WANG, Jing MIN, Jing HU, Zehan WANG, Xiuguo CHEN, Zirong TANG, Shiyuan LIU. Femtosecond laser-acoustic modeling and simulation for AlCu nanofilm nondestructive testing[J]. Front. Mech. Eng., 2024, 19(5): 33-.
[2]	Minjie SHAO, Tielin SHI, Qi XIA. An M-VCUT level set-based data-driven model of microstructures and optimization of two-scale structures[J]. Front. Mech. Eng., 2024, 19(4): 26-.
[3]	Qi LI, Lei DING, Xin LUO. Dynamic motion of quadrupedal robots on challenging terrain: a kinodynamic optimization approach[J]. Front. Mech. Eng., 2024, 19(3): 20-.
[4]	Shengjie XIAO, Yongqi SHI, Zemin WANG, Zhe NI, Yuhang ZHENG, Huichao DENG, Xilun DING. Lift system optimization for hover-capable flapping wing micro air vehicle[J]. Front. Mech. Eng., 2024, 19(3): 19-.
[5]	Kun XU, Xinghan ZHUANG, Zhou SU, Qiuhong LIN, Shouzhi REN, Hang XIAO, Xilun DING. A novel stiffness optimization model of space telescopic boom based on locking mechanism[J]. Front. Mech. Eng., 2024, 19(3): 18-.
[6]	Ye TIAN, Tielin SHI, Qi XIA. Buckling optimization of curvilinear fiber-reinforced composite structures using a parametric level set method[J]. Front. Mech. Eng., 2024, 19(1): 9-.
[7]	Yingjun WANG, Zhenbiao GUO, Jianghong YANG, Xinqing LI. Multiresolution and multimaterial topology optimization of fail-safe structures under B-spline spaces[J]. Front. Mech. Eng., 2023, 18(4): 52-.
[8]	Xin PAN, Haoyu ZHANG, Jinji GAO, Congcong XU, Dongya LI. Radial electromagnetic type unbalance vibration self-recovery regulation system for high-end grinding machine spindles[J]. Front. Mech. Eng., 2023, 18(3): 47-.
[9]	Yi YAN, Xiaopeng ZHANG, Jiaqi HE, Dazhi WANG, Yangjun LUO. Achieving desired nodal lines in freely vibrating structures via material-field series-expansion topology optimization[J]. Front. Mech. Eng., 2023, 18(3): 42-.
[10]	Yunpeng YIN, Yue ZHAO, Yuguang XIAO, Feng GAO. Footholds optimization for legged robots walking on complex terrain[J]. Front. Mech. Eng., 2023, 18(2): 26-.
[11]	Aodi YANG, Shuting WANG, Nianmeng LUO, Tifan XIONG, Xianda XIE. Massively efficient filter for topology optimization based on the splitting of tensor product structure[J]. Front. Mech. Eng., 2022, 17(4): 54-.
[12]	Jianzhao WU, Chaoyong ZHANG, Kunlei LIAN, Jiahao SUN, Shuaikun ZHANG. Processing parameter optimization of fiber laser beam welding using an ensemble of metamodels and MOABC[J]. Front. Mech. Eng., 2022, 17(4): 47-.
[13]	Junhui ZHANG, Yining SHEN, Minyao GAN, Qi SU, Fei LYU, Bing XU, Yuan CHEN. Multi-objective optimization of surface texture for the slipper/swash plate interface in EHA pumps[J]. Front. Mech. Eng., 2022, 17(4): 48-.
[14]	Rujun FAN, Yunhua LI, Liman YANG. Multiobjective trajectory optimization of intelligent electro-hydraulic shovel[J]. Front. Mech. Eng., 2022, 17(4): 50-.
[15]	Zhaokun ZHANG, Zhufeng SHAO, Zheng YOU, Xiaoqiang TANG, Bin ZI, Guilin YANG, Clément GOSSELIN, Stéphane CARO. State-of-the-art on theories and applications of cable-driven parallel robots[J]. Front. Mech. Eng., 2022, 17(3): 37-.

Viewed

Full text

Abstract

Cited

Shared

Discussed