锻压技术

锻压机床用转台轴承摩擦力矩研究

英文标题：Research on friction torque of YRT turntable bearing for forging machine

作者：张开哲张占立王恒迪张文虎刘延斌

单位：河南科技大学

分类号：TH133.33+2

出版年,卷(期):页码：2020,45(8):134-140

摘要：

摩擦力矩是转台轴承的重要性能参数之一。针对转台轴承摩擦力矩精确建模问题，基于BP神经网络与二阶响应曲面法，提出了一套新的预测转台轴承摩擦力矩的方法体系，评估了两种模型的决定系数R2、均方根误差RMSE和平均绝对误差MAE。结果表明：BP神经网络的拟合度优于二阶响应曲面法。YRT100转台轴承摩擦力矩的测试试验表明：传统力矩计算模型、二阶响应曲面模型和BP神经网络模型的平均误差分别为19.72%，9.76%和4.42%，说明BP神经网络模型的预测精度较高。研究结果为数控转台的摩擦补偿及转台轴承的低摩擦优化设计提供了一定的理论依据。

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.

基金项目：

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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