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Milling process stability detection for curved workpiece based on MVMD and LSTM

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Abstract

The fundamental purpose of milling process detection is to precisely predict chatter, which is the principal factor affecting the cutting process stability. This study proposes a cutting process detection approach for curved parts based on cutting force signals, splitting the cutting process into four states: no cutting, stable cutting, transition cutting, and chatter cutting. First, multichannel cutting force signals were employed to compensate for the fact that unidirectional cutting force signals cannot accurately describe the cutting condition of curved components. Then, cutting force components (intrinsic mode functions) were obtained through the simultaneous decomposition of multi-channel cutting force signals using multivariate variational mode decomposition (MVMD), and the time-domain characteristics of cutting force components were calculated using the improved waveform factor and margin factor calculation methods. The long short-term memory (LSTM) neural network was trained to create a milling process detection model based on labeled features identified by FFT and cutting surface. Comparing the obtained detection results with the training results of the original signal features without MVMD decomposition and the training results of the single-channel signal features with MVMD reveals that the method can accurately recognize the cutting process of curved parts with a superior recognition effect.

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All data generated or analyzed during this study are included in this article.

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Funding

This study is supported by the National Natural Science Foundation of China (51805116).

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Authors and Affiliations

  1. School of Mechatronics Engineering, Harbin Institute of Technology, 92 West Dazhi Street, Harbin, 150001, China

    Hongyu Jin, Haiyong Sun, Hongya Fu & Zhenyu Han

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  1. Hongyu Jin
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  2. Haiyong Sun
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  3. Hongya Fu
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  4. Zhenyu Han
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Contributions

Hongyu Jin and Zhenyu Han conceived and designed the study. Haiyong Sun, Hongya Fu, and Hongyu Jin performed the experiments. Hongyu Jin and Hongya Fu analyzed the data. All authors read and approved the manuscript.

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Correspondence to Zhenyu Han.

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Jin, H., Sun, H., Fu, H. et al. Milling process stability detection for curved workpiece based on MVMD and LSTM. Int J Adv Manuf Technol 123, 1025–1036 (2022). https://doi.org/10.1007/s00170-022-10030-8

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  • DOI: https://doi.org/10.1007/s00170-022-10030-8

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