A data-driven rolling optimization method for trajectory tracking error prediction of CNC machine tools  

作　　者：Yinxin GUAN Jixiang YANG Shizhong TAN Han DING 

机构地区：[1]School of Mechanical Science and Engineering,State Key Laboratory of Intelligent Manufacturing Equipment and Technology,Huazhong University of Science and Technology,Wuhan 430074,China

出　　处：《Science China(Technological Sciences)》2025年第1期270-285,共16页中国科学(技术科学英文版)

基　　金：supported by the National Natural Science Foundation of China(Grant Nos.52188102,52122512,52375496)。

摘　　要：The dynamic performance of the feed-drive system in CNC machine tools directly influences the accuracy of machined parts.To enhance the motion control performance of CNC machine tools,a high-precision model of the feed-drive system is critical.However,current modeling methods for feed-drive systems seldom consider time-varying factors such as loads,wear,and lubrication.As a result,the model accuracy degrades when the system characteristics are affected by these time-varying factors.In this paper,a rolling optimization method with partial weights frozen is developed to realize quick iterative learning of a data-driven model for a feed drive system with time-varying characteristics using a small amount of data.First,the long short-term memory fully connected(LSTM-FC)network is built and divided into feature extraction and output fitting parts based on their functions.Then,a weight freezing-based rolling optimization method is applied.The weights in the feature extraction part are frozen,which preserves the learned common knowledge and patterns by solidifying the way that high-dimensional features are extracted from the input.By adjusting the weights in the output fitting part,the extracted highdimensional features are remapped to the new data distribution changed by time-varying factors.Finally,the performance of the developed rolling optimization method is confirmed by experiments.The results show that the proposed rolling optimization method reduces the maximum prediction errors by 49.5%and the total training time by 96.3%compared with existing methods,which demonstrates that the proposed method can restore model accuracy when the system characteristics change due to timevarying factors,and significantly accelerate the optimization process by rolling optimization.

关 键 词：rolling optimization feed-drive system DATA-DRIVEN TIME-VARYING machine tool 

分 类 号：TG659[金属学及工艺—金属切削加工及机床]