BP网络在进给系统定位误差预测中的运用  

A Forecasting Method of Positioning Accuracy for CNC Machine Tools Feed System Based on BP Neural Network

作　　者：邓超[1] 钱有胜 吴军[2] 熊尧[3] 段超群[1] 

机构地区：[1]华中科技大学机械学院制造装备数字化国家工程中心,武汉430074 [2]华中科技大学船舶与海洋工程学院,武汉430074 [3]武汉第二船舶设计研究所,武汉430070

出　　处：《振动．测试与诊断》2017年第3期449-455,共7页Journal of Vibration,Measurement & Diagnosis

基　　金：国家自然科学基金资助项目(51375181;51475189);国家重点研发计划政府间专项资助项目(2016YFE0121700)

摘　　要：针对机床进给伺服系统定位精度预测的难点,分析了进给伺服系统机械传动系统定位误差增长的原因,提出了一种定位误差预测的方法。在Adams中建立进给伺服系统动力学仿真模型,得到不同初始状态下的定位误差值,基于BP神经网络建立工作台与螺母座间隙、滚珠丝杠倾斜度、工件负载与定位误差之间的映射模型,根据映射模型提出对定位误差预测的方法。利用所建立的精密运动可靠性试验平台进行验证,证明了该方法的正确性和有效性。Aimed at the difficulty in forecasting the positioning accuracy of machine tool feed system,a new model has been established,analyzing the positioning accuracy degradation of the mechanical transmission system of feed system.Firstly,a computer numerical control(CNC)machine tool feed system model is made using a dynamic simulation software Adams to obtain positioning accuracy values for different initial state.Then,a mapping model is set up between the positioning accuracy and the gap,the ball tilt and the workpiece loading by BP neural network.According to the mapping model,the prediction method of the positioning accuracy is discussed in detail.Finaly,the method is proved to be correct and effective by conducting the test experiment of positioning accuracy on the test platform of precision motion reliability.

关 键 词：进给伺服系统 BP神经网络 映射模型 ADAMS 定位误差预测 

分 类 号：TH17[机械工程—机械制造及自动化] TP306[自动化与计算机技术—计算机系统结构]