Prediction of machining errors for free-form surfaces based on an improved grey model (1, N)

被引：0

作者：

Ma, Dayang ^{[1
]}

Chen, Yueping ^{[1
]}

机构：

[1] School of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou

来源：

International Journal of Metrology and Quality Engineering | 2024年 / 15卷

基金：

中国国家自然科学基金;

关键词：

Free-form surfaces; GM(1; N); Markov theory; Metabolism; Prediction of machining errors;

D O I：

10.1051/ijmqe/2024011

中图分类号：

学科分类号：

摘要：

An improved grey model (GM) for predicting free-form surface machining errors was established to address the low measuring efficiencies of coordinate measuring machines (CMMs) and the low prediction accuracy of the GM(1,1) model. A number of points on a free-form surface are measured with a CMM, and machining errors are obtained. Ideas from metabolic methods and the GM(1,N) prediction model were combined. To reduce the impact of random fluctuations of the machining errors, a Markov prediction model was then used to correct the fitted results for the residuals and obtain the predicted results of the machining errors for free-form surfaces, thus improving the prediction accuracy of the model. The predicted results of the metabolic GM(1,1) and metabolic GM(1,N) models were then compared. The experimental results showed that the combination of metabolic theory, a grey model, and Markov theory effectively improved the prediction accuracy. © 2024 D. Ma and Y. Chen, Published by EDP Sciences.

引用

共 24 条

[1]

Marek M., Selection of the best model of distribution of measurement points in contact coordinate measurements of free-form surfaces of products, Sensors, 19, (2019)

[2]

Zhao D.Z., Wang W.H., Zhou J.Z., Et al., Measurement point sampling method for inspection of parts with free-form surfaces, Adv. Mech. Eng., 10, pp. 1-12, (2018)

[3]

Wolovich W., Albakri H., Yalcin H., The precise measurement of free-form surfaces, J. Manuf. Sci. Eng., 124, pp. 326-332, (2002)

[4]

Liu S.F., Zhou M.L., Wang J.P., Research on variation model for predicting machining error of milling surface, Manuf. Technol. Machine Tool., 4, pp. 80-84, (2018)

[5]

Gong L.X., Jiang J.H., A BP neural networks error predictive model of mechanical processing based on L-M algorithm, Machine Tool Hydraul, 11, pp. 67-71, (2013)

[6]

Zhang Q., Wen H.X., Study on the optimization of machining error gray forecast modeling, Machine Tool Hydraul, 36, pp. 61-63, (2008)

[7]

Qi S.H., Fei Y.T., Prediction of error in manufacturing based on the gray model theory, Trans. Chin. Soc. Agric. Machinery., 32, (2001)

[8]

Sun H., Peng F.Y., Zhou L., Et al., A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts, Int. J. Mech. Sci., 192, pp. 106-111, (2021)

[9]

Wang B., Predicting machining errors by artificial neural network, J. Jiangsu Univ. Sci. Technol., 4, pp. 44-48, (1997)

[10]

Huang K.Q., Chen Y.P., Zhang Y.K., Machining errors prediction of free-form surfaces: BP neural network algorithm optimized by simulated annealing algorithm, J. Guangxi Univ. Sci. Technol., 33, (2022)

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