Spindle Thermal Error Optimization Modeling of a Five-axis Machine Tool

作     者：Qianjian GUO Shuo FAN Rufeng XU Xiang CHENG Guoyong ZHAO Jianguo YANG 

作者机构：School of Mechanical Engineering Shandong University of Technology School of Mechanical Engineering Shanghai Jiao Tong University 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2017年第30卷第3期

页      面：746-753页

核心收录：

学科分类：08[工学] 080202[工学-机械电子工程] 0817[工学-化学工程与技术] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0811[工学-控制科学与工程] 080201[工学-机械制造及其自动化] 0801[工学-力学（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China(Grant No.51305244) Shandong Provincal Natural Science Foundation of China(Grant No.ZR2013EEL015) 

主　　题：Five-axis machine tool Artificial bee colony Thermal error modeling Artificial neural network 

摘      要：Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.