Transactions of Materials and Heat Treatment

Title：Research on friction torque of YRT turntable bearing for forging machine

Authors： Zhang Kaizhe Zhang Zhanli Wang Hengdi Zhang Wenhu Liu Yanbin

Unit： Henan University of Science and Technology

KeyWords： CNC turntable turntable bearing friction torque BP neural network the second-order response surface method

ClassificationCode：TH133.33+2

year,vol(issue):pagenumber：2020,45(8):134-140

Abstract：

Friction torque is one of the important performance parameters of turntable bearing. For the accurate modeling of friction torque for turntable bearing, a new method for predicting the friction torque of turntable bearing was proposed based on the BP neural network and the second-order response surface method, and the decision coefficient R2, the mean square error RMSE and the mean absolute error MAE of two models were evaluated. The results show that the fitting degree of the BP neural network is better than that of the second-order response surface method. The test results of friction torque for YRT100 turntable bearing show that the average errors of the traditional torque calculation model, the second-order response surface model and the BP neural network model are 19.72%, 9.76% and 4.42% respectively, which indicates that the BP neural network model has higher prediction accuracy. Thus, the research results provide a certain theoretical basis for the friction compensation of CNC turntable and the low friction optimization and design of turntable bearing.

Funds：

国家自然科学基金青年科学基金资助项目(51905152)；河南科技大学研究生创新基金项目(CXJJ-2019-KJ10); 河南省自然科学基金资助项目(182300410273)

张开哲(1996-)，男，硕士研究生，E-mail：zhangkaizhe01@163.com；通讯作者：张占立(1965-)，男，博士，教授，硕士生导师，E-mail：13683843763@163.com

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