文献类型：期刊文献

英文题名：A support vector regression-based method for modeling geometric errors in CNC machine tools

作者：Zhang, Chuanjing[1];Liu, Huanlao[1];Zhou, Qunlong[1];Wang, Yulin[1]

机构：[1]Guangdong Ocean Univ, Sch Mech Engn, Zhanjiang 524088, Peoples R China

年份：2024

卷号：131

期号：5-6

起止页码：2691

外文期刊名：INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY

收录：SCI-EXPANDED(收录号：WOS:001063141900003)、、EI(收录号：20233714699997)、Scopus(收录号：2-s2.0-85170054859)、WOS

基金：This research is sponsored by the National Natural Science Foundation of China [grant number 52175458], the Natural Science Foundation of Guangdong Province of China [grant number 2021A15150110591], and the Department of Education of Guangdong Province of China [grant number 2022ZDZX3006].

语种：英文

外文关键词：Geometric errors; Support vector machine regression; Improved hybrid grey wolf optimization algorithm; CNC machine tools; Error compensation

外文摘要：For the problem of geometric error prediction of CNC machine tools, an improved hybrid grey wolf optimization (IHGWO) algorithm is proposed to optimize the geometric error modeling scheme of the support vector regression machine (SVR). The predicted and measured values of the geometric error are combined to construct the fitness function. In IHGWO, principles of particle swarm optimization (PSO) algorithm and dimension learning-based hunting (DLH) search strategies are introduced while retaining the excellent grey wolf position of the basic grey wolf optimization (GWO) algorithm. IHGWO algorithm uses Euclidean distance to construct the neighborhood of individual grey wolves, which enhances the ability to communicate between individual grey wolves and improves the convergence speed and accuracy of the algorithm. Predictive performance of SVR models using sum squared residual to quantify geometric error. Based on the screw theory, space models of geometric errors of CNC machine tools are established and combined with SVR models of geometric errors for compensation. Empirical evidence proves that the proposed method surpasses current error modeling methods in terms of precision and efficiency, as evidenced by a minimum reduction of 9% in circular trajectory error and a reduction to two overruns in S-shaped test pieces after error compensation. This research contributes to the field of CNC machine tool error modeling and has practical implications for manufacturing industries.