Enhancing Spindle Precision: Thermal Error Modeling with Multi-parameter Optimization and Energy Consumption Data

Thermal error prediction models for key components in precision computer numerical control machine tools often fail to maintain high accuracy under varying working conditions, significantly impairing the effectiveness of error compensation. Furthermore, existing models frequently disregard correlati...

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Vydáno v:	International journal of precision engineering and manufacturing Ročník 26; číslo 8; s. 1837 - 1853
Hlavní autoři:	Zhou, Bo, Chen, Guo-hua, Mao, Jie, Li, Bo, Fu, Zhen-xin, Li, Tao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Seoul Korean Society for Precision Engineering 01.08.2025 Springer Nature B.V
Témata:	Accuracy Adaptive algorithms Algorithms Boundary conditions Confidence intervals Density Design Energy consumption Engineering Error compensation Experiments Heat Industrial and Production Engineering Machine tools Materials Science Neural networks Numerical controls Optimization Parameter modification Prediction models Real time Regular Paper Sensors Software Spindles Working conditions MBSCPO Energy consumption data Electric spindle Thermal error modeling method Prediction interval
ISSN:	2234-7593, 2005-4602
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Abstract	Thermal error prediction models for key components in precision computer numerical control machine tools often fail to maintain high accuracy under varying working conditions, significantly impairing the effectiveness of error compensation. Furthermore, existing models frequently disregard correlations between thermal errors and non-temperature-related variables. Therefore, an electric spindle thermal error modeling method based on energy consumption data and a multi-parameter optimization algorithm is proposed in this study. Based on energy consumption data(current, power), monitoring of eight key temperature points, and real-time thermal error data, a network model integrating the Bidirectional Inception-based Temporal Convolutional Network, the Bidirectional Gated Recurrent Unit (BIGRU), and the multi-head attention mechanism is established. The model's hyper-parameters and kernel density are dynamically optimized through the Modified Crested Porcupine Optimization algorithm and the Adaptive Kernel Density Estimation method. Through practical verification under variable working conditions (covering different rotational speeds), compared with the BIGRU model, the coefficient of determination (R 2 ) of the proposed model is increased by 25%. Meanwhile, the Prediction Interval at 95% confidence level with Asymmetric Width of the proposed model is 23%, highlighting the superiority of this modeling method in terms of robustness and generalization ability.
AbstractList	Thermal error prediction models for key components in precision computer numerical control machine tools often fail to maintain high accuracy under varying working conditions, significantly impairing the effectiveness of error compensation. Furthermore, existing models frequently disregard correlations between thermal errors and non-temperature-related variables. Therefore, an electric spindle thermal error modeling method based on energy consumption data and a multi-parameter optimization algorithm is proposed in this study. Based on energy consumption data(current, power), monitoring of eight key temperature points, and real-time thermal error data, a network model integrating the Bidirectional Inception-based Temporal Convolutional Network, the Bidirectional Gated Recurrent Unit (BIGRU), and the multi-head attention mechanism is established. The model's hyper-parameters and kernel density are dynamically optimized through the Modified Crested Porcupine Optimization algorithm and the Adaptive Kernel Density Estimation method. Through practical verification under variable working conditions (covering different rotational speeds), compared with the BIGRU model, the coefficient of determination (R2) of the proposed model is increased by 25%. Meanwhile, the Prediction Interval at 95% confidence level with Asymmetric Width of the proposed model is 23%, highlighting the superiority of this modeling method in terms of robustness and generalization ability. Thermal error prediction models for key components in precision computer numerical control machine tools often fail to maintain high accuracy under varying working conditions, significantly impairing the effectiveness of error compensation. Furthermore, existing models frequently disregard correlations between thermal errors and non-temperature-related variables. Therefore, an electric spindle thermal error modeling method based on energy consumption data and a multi-parameter optimization algorithm is proposed in this study. Based on energy consumption data(current, power), monitoring of eight key temperature points, and real-time thermal error data, a network model integrating the Bidirectional Inception-based Temporal Convolutional Network, the Bidirectional Gated Recurrent Unit (BIGRU), and the multi-head attention mechanism is established. The model's hyper-parameters and kernel density are dynamically optimized through the Modified Crested Porcupine Optimization algorithm and the Adaptive Kernel Density Estimation method. Through practical verification under variable working conditions (covering different rotational speeds), compared with the BIGRU model, the coefficient of determination (R 2 ) of the proposed model is increased by 25%. Meanwhile, the Prediction Interval at 95% confidence level with Asymmetric Width of the proposed model is 23%, highlighting the superiority of this modeling method in terms of robustness and generalization ability.
Author	Li, Bo Li, Tao Mao, Jie Zhou, Bo Chen, Guo-hua Fu, Zhen-xin
Author_xml	– sequence: 1 givenname: Bo surname: Zhou fullname: Zhou, Bo organization: School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang Municipal Key Laboratory of Precision Manufacturing and Special Processing Technology – sequence: 2 givenname: Guo-hua surname: Chen fullname: Chen, Guo-hua organization: School of Mechanical Engineering, Hubei University of Arts and Science, XY-HUST Advanced Manufacturing Engineering Research Institute, Xiangyang Municipal Key Laboratory of Precision Manufacturing and Special Processing Technology – sequence: 3 givenname: Jie surname: Mao fullname: Mao, Jie organization: School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang Municipal Key Laboratory of Precision Manufacturing and Special Processing Technology – sequence: 4 givenname: Bo surname: Li fullname: Li, Bo organization: School of Mechanical Engineering, Hubei University of Arts and Science, XY-HUST Advanced Manufacturing Engineering Research Institute – sequence: 5 givenname: Zhen-xin surname: Fu fullname: Fu, Zhen-xin organization: School of Mechanical Engineering, Hubei University of Arts and Science – sequence: 6 givenname: Tao orcidid: 0009-0006-1883-9973 surname: Li fullname: Li, Tao email: 330966532@qq.com organization: School of Mechanical and Automotive Engineering, Science and Technology College of Hubei University of Arts and Science
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SubjectTerms	Accuracy Adaptive algorithms Algorithms Boundary conditions Confidence intervals Density Design Energy consumption Engineering Error compensation Experiments Heat Industrial and Production Engineering Machine tools Materials Science Neural networks Numerical controls Optimization Parameter modification Prediction models Real time Regular Paper Sensors Software Spindles Working conditions
Title	Enhancing Spindle Precision: Thermal Error Modeling with Multi-parameter Optimization and Energy Consumption Data
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