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Online tool condition monitoring in micromilling using LSTM

Author

Listed:
  • Ashish Manwar

    (Indian Institute of Technology Bombay)

  • Alwin Varghese

    (Tvarit Solutions Pvt. Ltd)

  • Sumant Bagri

    (Indian Institute of Technology Bombay)

  • Suhas S. Joshi

    (Indian Institute of Technology Bombay
    Indian Institute of Technology Indore)

Abstract

High-quality and cost-effective production in micro-milling involves the use of tools of diameter 50–800 $$\mu $$ μ m, at high rotational speeds, along complex tool paths. These tools are susceptible to high wear and unexpected breakage, and hence a high-precision tool condition monitoring system is required to predict the tool wear states. In this work, we propose a novel approach for high-precision tool condition monitoring in micro-milling using cutting force signals. The method correlates dominant frequency variations with the tool condition along its complete life cycle, considering both straight and circular tool paths to mimic real-life machining scenarios. Therefore, using multiple micro-milling experiments, dominant frequency was characterized using Wavelet transform and Short Time Fourier Transform, and a tool condition prognostic model was developed using LSTM networks. The model accurately predicts force signals with an RMSE less than 0.09, enabling indirect prediction of the tool condition.

Suggested Citation

  • Ashish Manwar & Alwin Varghese & Sumant Bagri & Suhas S. Joshi, 2025. "Online tool condition monitoring in micromilling using LSTM," Journal of Intelligent Manufacturing, Springer, vol. 36(2), pages 935-955, February.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:2:d:10.1007_s10845-023-02273-3
    DOI: 10.1007/s10845-023-02273-3
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