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Intermittent multivariate time series spindle thermal error prediction under wide environmental temperature ranges and diverse scenario conditions

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Abstract

As the integration of mechanical engineering and deep learning fields becomes increasingly intertwined, the application of experimental thermal error modeling in intelligent manufacturing has gained significant importance. In this paper, the issue of spindle thermal error is treated as a multivariate time series problem due to the thermal transfer characteristics. This study aims to address the challenge of modeling intermittent multivariate time series spindle thermal errors under a wide range of environmental temperatures and various operational scenarios. To tackle this challenge, a substantial volume of experimental data, capable of effectively reflecting the patterns of spindle thermal error variations, was collected through experiments conducted at multiple speeds and under various operational scenarios. Subsequently, the acquired thermal error data underwent intermittent multivariate time series transformation (IMTS) to suit the serialized deep learning model. The study introduces the Crossformer model into the field of thermal error modeling for the first time, which is a variant of the Transformer model. The Crossformer model exhibits remarkable adaptability to temporal aspects while effectively maintaining its focus on data features. Ultimately, this study resulted in the development of the IMTS-CrossformerR experimental thermal error model. Throughout the research, a comprehensive examination of various models was undertaken, including two traditional Transformer models and other thermal error deep learning and machine learning models. The results indicate that the proposed model outperforms its counterparts across multiple model metrics and predictive capabilities. Particularly noteworthy is its substantial improvement in the range (± 5) ratio of residual fluctuations reaching 95.7%, a key engineering metric.

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Data availability

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

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Funding

This work was supported by the Ministry of Science and Technology (No. 2018YFB1306802) and the National Natural Science Foundation of China (Nos. 51975344 and 52075337).

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Authors and Affiliations

  1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China

    Guangjie Jia & Xu Zhang

  2. Shanghai Key Laboratory of Networked Manufacturing and Enterprise Information, Shanghai Jiao Tong University, Shanghai, 200240, China

    Yijun Shen & Nuodi Huang

Authors
  1. Guangjie Jia
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  2. Xu Zhang
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  3. Yijun Shen
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  4. Nuodi Huang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Guangjie Jia, Xu Zhang, and Nuodi Huang. The first draft of the manuscript was written by Guangjie Jia, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Nuodi Huang.

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Jia, G., Zhang, X., Shen, Y. et al. Intermittent multivariate time series spindle thermal error prediction under wide environmental temperature ranges and diverse scenario conditions. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13652-2

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