Intelligent manufacturing systems for on-line tool wear monitoring under cross conditions: a brief review

Kaile Ma; Guofeng Wang

doi:10.1117/12.3039225

5 August 2024 Intelligent manufacturing systems for on-line tool wear monitoring under cross conditions: a brief review

Kaile Ma, Guofeng Wang

Author Affiliations +

Proceedings Volume 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024); 132264M (2024) https://doi.org/10.1117/12.3039225
Event: 3rd International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 2024, Changsha, China

Abstract

Tool wear monitoring (TWM) based on data-driven methods is essential to ensure product quality and overall efficiency. Machine learning (ML) models, unlike physics-based approaches, are practical for online monitoring without interrupting the machining processes. Existing reviews on TWM mainly focus on a comprehensive perspective or partial aspects. This paper reviews the development of shallow machine learning (SML) and deep learning (DL) techniques for smart tool monitoring under cross conditions. The mainstream framework of online TWM is introduced. The intelligent tool monitoring, which is based on SML and DL, was reviewed and categorized into two tasks: tool condition classification and tool wear prediction. The advantages and disadvantages of SML and DL were subsequently discussed. Finally, the challenges of smart tool monitoring for practical engineering application including sensor selection, few samples, model reliability and online update were outlined.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Kaile Ma and Guofeng Wang "Intelligent manufacturing systems for on-line tool wear monitoring under cross conditions: a brief review", Proc. SPIE 13226, Third International Conference on Advanced Manufacturing Technology and Manufacturing Systems (ICAMTMS 2024), 132264M (5 August 2024); https://doi.org/10.1117/12.3039225

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