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A thermal deformation estimation method for high precision machine tool spindles based on the convolutional neural network

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Abstract

The temperature rise of key internal components and ambient temperature changes during machining processes are the main causes of thermal displacement in machine tool spindle. This wastes materials and reduces working efficiency. A highly accurate and robust thermal spindle displacement estimation scheme is proposed in this paper which is based on the convolutional neural network (CNN) technique. Several key signals of specific dimensions related to spindle thermal displacement were first collected as two-dimensional (2D) signal maps. A multi-level feature expression for these 2D signal maps was extracted using convolution and pooling. A relationship between the extracted features and spindle thermal displacement was then learned using the neural architecture of the full connection layer. Optimized hyperparameter settings were determined by design of experiments (DoE) applied to the proposed CNN model. Experimental results showed that the proposed method had better performance than the multiple regression analysis (MLR) or back propagation neural network (BPNN) methods in terms of estimation accuracy and robustness at different spindle speeds.

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Acknowledgments

This study was supported in part by grants from the Ministry of Science and Technology of the Republic of China (Taiwan) (Grant No. MOST 111-2221-E-167-020).

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan

    Chien-Wei Liao & Yu-Chi Liu

  2. Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan

    Ming-Tsang Lee

Authors
  1. Chien-Wei Liao
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  2. Ming-Tsang Lee
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  3. Yu-Chi Liu
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Corresponding author

Correspondence to Yu-Chi Liu.

Additional information

Chien-Wei Liao is a Master of the Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan. His research interests are deep learning and thermal error compensation of machine tools.

Ming-Tsang Lee is currently a faculty member in Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan. He received his Ph.D. in Mechanical Engineering from the University of California, Berkeley. His research interests encompass advanced heat transfer, laser-assisted manufacturing, intelligent machine tools, and green energy.

Yu-Chi Liu is an Assistant Professor of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan. His research interests include the application of artificial intelligence, image processing, software engineering, as well as the visualization, manufacturing accuracy and control of smart manufacturing.

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Liao, CW., Lee, MT. & Liu, YC. A thermal deformation estimation method for high precision machine tool spindles based on the convolutional neural network. J Mech Sci Technol 37, 3151–3162 (2023). https://doi.org/10.1007/s12206-023-0539-x

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  • DOI: https://doi.org/10.1007/s12206-023-0539-x

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