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Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model

Author

Listed:
  • Kamran Javed

    (FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBM)

  • Rafael Gouriveau

    (FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBM)

  • Xiang Li

    (Singapore Institute of Manufacturing Technology)

  • Noureddine Zerhouni

    (FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBM)

Abstract

In a high speed milling operation the cutting tool acts as a backbone of machining process, which requires timely replacement to avoid loss of costly workpiece or machine downtime. To this aim, prognostics is applied for predicting tool wear and estimating its life span to replace the cutting tool before failure. However, the life span of cutting tools varies between minutes or hours, therefore time is critical for tool condition monitoring. Moreover, complex nature of manufacturing process requires models that can accurately predict tool degradation and provide confidence for decisions. In this context, a data-driven connectionist approach is proposed for tool condition monitoring application. In brief, an ensemble of Summation Wavelet-Extreme Learning Machine models is proposed with incremental learning scheme. The proposed approach is validated on cutting force measurements data from Computer Numerical Control machine. Results clearly show the significance of our proposition.

Suggested Citation

  • Kamran Javed & Rafael Gouriveau & Xiang Li & Noureddine Zerhouni, 2018. "Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model," Journal of Intelligent Manufacturing, Springer, vol. 29(8), pages 1873-1890, December.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:8:d:10.1007_s10845-016-1221-2
    DOI: 10.1007/s10845-016-1221-2
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    References listed on IDEAS

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    1. An, Dawn & Kim, Nam H. & Choi, Joo-Ho, 2015. "Practical options for selecting data-driven or physics-based prognostics algorithms with reviews," Reliability Engineering and System Safety, Elsevier, vol. 133(C), pages 223-236.
    2. Hu, Chao & Youn, Byeng D. & Wang, Pingfeng & Taek Yoon, Joung, 2012. "Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life," Reliability Engineering and System Safety, Elsevier, vol. 103(C), pages 120-135.
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    Cited by:

    1. Dawei Zhao & Mikhail Ivanov & Yuanxun Wang & Wenhao Du, 2021. "Welding quality evaluation of resistance spot welding based on a hybrid approach," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1819-1832, October.
    2. Danil Yu Pimenov & Andres Bustillo & Szymon Wojciechowski & Vishal S. Sharma & Munish K. Gupta & Mustafa Kuntoğlu, 2023. "Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2079-2121, June.
    3. Dongbo Wu & Hui Wang & Kaiyao Zhang & Bing Zhao & Xiaojun Lin, 2020. "Research on adaptive CNC machining arithmetic and process for near-net-shaped jet engine blade," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 717-744, March.
    4. Yuqing Zhou & Bintao Sun & Weifang Sun & Zhi Lei, 2022. "Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 247-258, January.

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