IPRR: thermal error modeling for CNC machines based on individual penalized ridge regression

作     者：Wei, Xinyuan Zhang, Nan Zhou, Jinghuan Ye, Honghan 

作者机构：Anhui Univ Technol Sch Elect & Informat Engn Maanshan 243032 Anhui Peoples R China Solventum Co Hlth Informat Syst 2510 Conway Ave St Paul MN 55144 USA Anhui Univ Technol Anhui Prov Engn Lab Intelligent Demolit Equipment Maanshan 243032 Anhui Peoples R China Anhui Univ Technol Anhui Prov Key Lab Special Heavy Load Robot Maanshan 243032 Anhui Peoples R China 

出 版 物：《INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY》 (Int J Adv Manuf Technol)

年 卷 期：2025年第136卷第5-6期

页      面：2745-2755页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 08[工学] 0802[工学-机械工程] 0811[工学-控制科学与工程] 

基　　金：Anhui Provincial Key Research and Development Project of China [2022f04020005] Scientific Research Key Project of Anhui Provincial University of China [2022AH050313] Open Project of Anhui Province Engineering Laboratory of Intelligent Demolition Equipment [APELIDE2023A008] Open Project of Anhui Province Key Laboratory of Special Heavy Load Robot [TZJQR010-2024] 

主　　题：CNC machine tools Individual penalized ridge regression Prediction accuracy Robustness Thermal error 

摘      要：Thermal errors have become the main factor affecting the machine tool accuracy. Statistical prediction and compensation models are commonly established based on the measured temperature data to reduce thermal errors. Current literature typically focuses on first selecting temperature-sensitive points (TSPs) to reduce multicollinearity and then building thermal error models for CNC machines. Thus, this two-step approach loses useful information after the variable selection and prevents thermal error models from using this lost information. In addition, there are few approaches that can simultaneously reduce multicollinearity through variable selection and model thermal errors in one single step. Therefore, to fill the research gap, a one-shot thermal error modeling and prediction approach is proposed based on the individually penalized ridge regression (IPRR). Specifically, the traditional ridge regression method, which intrinsically fails to select TSPs, is adopted and modified by setting up individually penalized ridge parameters for each variable, thereby achieving both variable selection and thermal error modeling simultaneously. Then, the proposed IPRR algorithm is compared using the experimental data with the existing methods. The comparison results show that the IPRR algorithm can significantly improve the prediction accuracy by 10% and robustness by 40% on thermal errors. Finally, the thermal error compensation experiments are conducted on the experimental object to show the practicability of the IPRR algorithm.