Online tool condition monitoring in micromilling using LSTM

Ashish Manwar, Alwin Varghese, Sumant Bagri and Suhas S. Joshi ()
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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

Journal of Intelligent Manufacturing, 2025, vol. 36, issue 2, No 9, 935-955

Abstract: 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.

Keywords: Dominant frequency analysis; LSTM; Micro-milling; Complex tool paths; Tool wear (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10845-023-02273-3

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