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Random forests based classification of tool wear using vibration signals and wear area estimation from tool image data

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Abstract

In precision manufacturing, tool condition monitoring is critical for improving surface finish, increasing efficiency, and lowering manufacturing costs. The present work discusses a complete workflow to accurately predict the tool condition based on vibration data obtained during the turning operation performed on a lathe. An image processing methodology is applied to compute the tool wear area. A specialized misclassification cost matrix is used to train the random forests algorithm to improve the classification of tool conditions. This model can correctly classify tool condition from vibration signals of 0.5 s with an accuracy of 97%. Furthermore, this investigation can be modified to suit the real-world classification of the tool condition based on tool wear requirements.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Birla Institute of Technology and Science - Pilani, Hyderabad, Telangana, 500078, India

    Basil Cardoz, Haris Naiyer E Azam Shaikh, Shoaib Munir Mulani, Ashwani Kumar & Sabareesh Geetha Rajasekharan

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  1. Basil Cardoz
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  2. Haris Naiyer E Azam Shaikh
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Cardoz, B., Shaikh, H.N.E., Mulani, S.M. et al. Random forests based classification of tool wear using vibration signals and wear area estimation from tool image data. Int J Adv Manuf Technol 126, 3069–3081 (2023). https://doi.org/10.1007/s00170-023-11173-y

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