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A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds

Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Th...

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Detalles Bibliográficos
Autores principales: Wei, Xinyuan, Ye, Honghan, Zhou, Jinghuan, Pan, Shujing, Qian, Muyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224392/
https://www.ncbi.nlm.nih.gov/pubmed/37430830
http://dx.doi.org/10.3390/s23104916
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author Wei, Xinyuan
Ye, Honghan
Zhou, Jinghuan
Pan, Shujing
Qian, Muyun
author_facet Wei, Xinyuan
Ye, Honghan
Zhou, Jinghuan
Pan, Shujing
Qian, Muyun
author_sort Wei, Xinyuan
collection PubMed
description Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method.
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spelling pubmed-102243922023-05-28 A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds Wei, Xinyuan Ye, Honghan Zhou, Jinghuan Pan, Shujing Qian, Muyun Sensors (Basel) Article Establishing a mathematical model to predict and compensate for the thermal error of CNC machine tools is a commonly used approach. Most existing methods, especially those based on deep learning algorithms, have complicated models that need huge amounts of training data and lack interpretability. Therefore, this paper proposes a regularized regression algorithm for thermal error modeling, which has a simple structure that can be easily implemented in practice and has good interpretability. In addition, automatic temperature-sensitive variable selection is realized. Specifically, the least absolute regression method combined with two regularization techniques is used to establish the thermal error prediction model. The prediction effects are compared with state-of-the-art algorithms, including deep-learning-based algorithms. Comparison of the results shows that the proposed method has the best prediction accuracy and robustness. Finally, compensation experiments with the established model are conducted and prove the effectiveness of the proposed modeling method. MDPI 2023-05-19 /pmc/articles/PMC10224392/ /pubmed/37430830 http://dx.doi.org/10.3390/s23104916 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Wei, Xinyuan
Ye, Honghan
Zhou, Jinghuan
Pan, Shujing
Qian, Muyun
A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title_full A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title_fullStr A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title_full_unstemmed A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title_short A Regularized Regression Thermal Error Modeling Method for CNC Machine Tools under Different Ambient Temperatures and Spindle Speeds
title_sort regularized regression thermal error modeling method for cnc machine tools under different ambient temperatures and spindle speeds
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10224392/
https://www.ncbi.nlm.nih.gov/pubmed/37430830
http://dx.doi.org/10.3390/s23104916
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