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Open Access

Dimensional accuracy compensation method of large shaft grinding via residual error iteration with fuzzy approach

Changjie CHENa,bLiping WANGa,bShuailei FUa,bDong WANGa,b( )Xuekun LIa,b( )Baojun LIANGc
State Key Laboratory of Tribology in Advanced Equipment and Institute of Manufacturing Engineering, Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China
Beijing Key Lab of Precision/Ultra-precision Manufacturing Equipment and Control, Beijing 100084, China
Hiecise Precis Equipment Co., Ltd, Suzhou 215337, China

Peer review under responsibility of Editorial Committee of JAMST

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Abstract

Dimensional accuracy is one of the most important quality indicators of large shaft grinding, which directly affects the shaft service performance. Overcut/undercut caused by grinding wheel width and wear are the main factors affecting dimensional accuracy of large shaft grinding, which can be described by establishing physical models, and the physical models would be further used for compensation. However, the residual error always exists due to modeling uncertainty, and the residual error has nonlinear relation with compensation value, which is hard to completely eliminate. In order to solve the problem, this paper proposes a dimensional accuracy compensation method of large shaft grinding via residual error iteration with fuzzy approach. Two physical models are firstly established by considering the grinding wheel width and wear, respectively. The residual error after using these two models is further dealt with iteration, and the fuzzy approach is applied to dynamically calculate the compensation coefficients to improve dimensional accuracy while ensuring con-vergence. The experimental results show that the mean dimensional error is reduced 83% by using the pro-posed method, which is much better than other compensation methods.

References

1

Wei X, Li B, Chen L. Tool setting error compensation in large aspherical mirror grinding. The International Journal of Advanced Manufacturing Technology 2018;94(9-12):4093-4103.

2

Wang C, Wang D, Wang L. The development of time-dependent compensation model for roller CVC Profile generation in precision grinding. The International Journal of Advanced Manufacturing Technology 2021;114(5-6):1671-84.

3

Peng Y, Dai Y, Song C. Tool deflection model and profile error control in helix path contour grinding. International Journal of Machine Tools and Manufacture 2016;111:1-8.

4

Tönshoff HK, Peters J, Inasaki I. Modelling and Simulation of Grinding Processes. CIRP Annals 1992;41(2):677-88.

5

Hou ZB, Komanduri R. On the Mechanics of the grinding process -Part I. stochastic nature of the grinding process. International Journal of Machine Tools and Manufacture 2003;43(15):1579-93

6

Kurfess TR, Whitney DE, Brown ML. Verification of a dynamic grinding model. Journal of Dynamic Systems, Measurement, and Control 1988; 110(4):403-9.

7

Torrance AA, Badger JA. The relation between the traverse dressing of vitrified grinding wheels and their performance. International Journal of Machine Tools and Manufacture 2000; 40(12):1787-1811.

8

Wu W, Li C, Yang M. Specific energy and G ratio of grinding cemented carbide under different cooling and lubrication conditions. The International Journal of Advanced Manufacturing Technology 2019;105(1-4):67-82.

9

Kwak JS, Ha MK. Evaluation of wheel life by grinding ratio and static force. KSME International Journal 2002;16(9):1072-77.

10

Wang JM, Lou DY, Wang J. The study on grinding ratio in form grinding with White Fused Alumina (WA) grinding wheels. IOP Conference Series: Materials Science and Engineering 2018;317:012006.

11

Xiao G, Song K, Liu S. Comprehensive investigation into the effects of relative grinding direction on abrasive belt grinding process. Journal of Manufacturing Processes 2021;62:753-61.

12

Weck M, Hennes N, Schulz A. Dynamic behaviour of cylindrical traverse grinding processes. CIRP Annals 2001;50(1):213-16.

13

Liu Z, Tang Q, Liu N. A profile error compensation method in precision grinding of screw rotors. The International Journal of Advanced Manufacturing Technology 2019;100(9-12):2557-67.

14

Lin S, Jiang Z, Yin Y. Research on arc-shaped wheel wear and error compensation in arc envelope grinding. The International Journal of Advanced Manufacturing Technology 2019;103(5-8):1847-59.

15

Ou Y, Xing YS, Wang K. Investigation of crucial geometric errors of screw grinder for ball screw profile parameters. The International Journal of Advanced Manufacturing Technology 2022; 118(1-2): 533-550.

16

Yin SH, Chen FJ, Wang Y. Error compensation in one-point inclinedaxis nanogrinding mode for small aspheric mould. Advanced Materials Research 2010; 97-101:4206-4212.

17

Xu L, Fan F, Hu Y. A vision-based processing methodology for profile grinding of contour surfaces. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 2020;234(1-2):27-39.

18

Lee WC, Lee YT, Wei CC. Automatic error compensation for freeform surfaces by using on-machine measurement data. Applied Sciences 2019;9(15):3073.

19

Xu G, Liu X, Zhao J. Analysis of CVC roll contour and determination of roll crown. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material 2007;14(4):378.

Journal of Advanced Manufacturing Science and Technology
Cite this article:
CHEN C, WANG L, FU S, et al. Dimensional accuracy compensation method of large shaft grinding via residual error iteration with fuzzy approach. Journal of Advanced Manufacturing Science and Technology, 2023, 3(3): 2023008. https://doi.org/10.51393/j.jamst.2023008

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Received: 25 April 2023
Revised: 10 May 2023
Accepted: 31 May 2023
Published: 15 July 2023
© 2023 JAMST

This is an Open Access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0),which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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