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An axial attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions

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Abstract

Spindle thermal error is a major factor affecting the machining accuracy of machine tools. The time-consuming experiments required to model the thermal error of each machine tool spindle makes exhaustive studies difficult. Therefore, it is critically important to develop a transferable spindle thermal error prediction model that ensures robustness and accuracy, which provides theoretical guidance for the thermal error compensation and accuracy improvement of different machine tools. To achieve this, this paper proposes a migratory prediction method based on spatial–temporal axial attention bidirectional long short-term memory (axial attention-BiLSTM) network to predict the thermal error of computer numerical control (CNC) machine tool spindles under varying working conditions. By analyzing the mechanism of spindle thermal error generation, the spindle system is divided into multiple temperature regions, and the importance of each region is automatically determined by using the spatial attention mechanism. At the same time, considering the historical dependence of thermal error, BiLSTM is used to fuse the previous and following time series information and determine the weights of different time steps by temporal attention mechanism to strengthen the times series memory of thermal error prediction. An axial attention-BiLSTM model for thermal error prediction is built based on the spindle test bench. Compared with the BiLSTM and long short-term memory (LSTM) models, this model shows better and more stable prediction performance when migrated to various working conditions of horizontal CNC grinding machines.

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Acknowledgements

The authors thank the editor and referees for helping to improve this article.

Funding

This work is supported by the Key Research and Development Program of Zhejiang Province (NO.2021C01008) and National Natural Science Foundation of China (NO.52075480, U22A6001).

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Authors and Affiliations

  1. State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou, 310027, China

    Jiacheng Sun, Zhenyu Liu, Chan Qiu, Zhengyang Jiang, Haoyang Mao & Jianrong Tan

  2. Zhejiang Hangji Machine Tool Co., Ltd, Hangzhou, 310012, China

    Liang He

Authors
  1. Jiacheng Sun
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  2. Zhenyu Liu
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jiacheng Sun, Zhengyang Jiang, and Haoyang Mao. Experimental design and manuscript writing were guided by Zhenyu Liu, Chan Qiu, and Liang He. The first draft of the manuscript was written by Jiacheng Sun. Jianrong Tan and Zhenyu Liu contributed to funding acquisition.

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Correspondence to Zhenyu Liu.

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Sun, J., Liu, Z., Qiu, C. et al. An axial attention-BiLSTM-based method for predicting the migration of CNC machine tool spindle thermal error under varying working conditions. Int J Adv Manuf Technol 130, 1405–1419 (2024). https://doi.org/10.1007/s00170-023-12759-2

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  • DOI: https://doi.org/10.1007/s00170-023-12759-2

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