基于最小二乘配置的预行程误差模型  

Least Squares Collocation Based Pre-travel Error Model

作　　者：汤泽航 高健[1] 张揽宇 TANG Ze-hang;GAO Jian;ZHANG Lan-yu(State Key Laboratory of Precision Electronic Manufacturing Technology&Equipment,Guangdong University of Technology,Guangzhou 510006,China)

机构地区：[1]广东工业大学省部共建精密电子制造技术与装备国家重点实验室,广州510006

出　　处：《组合机床与自动化加工技术》2021年第7期11-14,共4页Modular Machine Tool & Automatic Manufacturing Technique

基　　金：国家自然科学基金项目(51675106);广东省重大研发专项(2018B090906002)。

摘　　要：为数控机床在线检测中预行程误差影响测量精度问题,对预行程误差的补偿方法进行了研究。在分析了在线检测系统中触测探针触发过程存在的预行程误差问题,提出了一种基于最小二乘配置法的预行程误差预测模型。并通过实际算例,验证了所提出的方法的精确性和有效性。同时与RBF神经网络模型的预测结果进行了对比分析。结果表明,最小二乘配置模型拟合残差较小,相应的在线检测预行程误差模型预测效果更好,预测精度相比高出38.86%。In order to solve the problem of the influence of the pre-travel error on the measurement accuracy in the on-machine measurement of CNC machine tools,the compensation method of the pre-travel error is studied.On the analysis of the problem of pre-travel error in OMM system,this paper proposes a compensation method which based on least square collocation method.A practical example is given to verify the accuracy and validity of the proposed method.At the same time,the prediction results of RBF neural network are compared and analyzed.The results show that the least-squares collocation model has smaller fitting residuals,and the corresponding Mathematical model has better prediction effect,and the prediction accuracy is 38.86%higher than the other algorithm.

关 键 词：触发式探针 在线检测 预测建模 最小二乘配置 

分 类 号：TH39[机械工程—机械制造及自动化] TG506[金属学及工艺—金属切削加工及机床]