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 

作者机构：School of Mechanical Science and EngineeringState Key Laboratory of Intelligent Manufacturing Equipment and TechnologyHuazhong University of Science and Technology 

出 版 物：《Science China Technological Sciences》 (中国科学:技术科学(英文版))

年 卷 期：2025年第1期

页      面：274-289页

核心收录：

学科分类：080202[工学-机械电子工程] 08[工学] 0802[工学-机械工程] 

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 ***, 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.