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

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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.

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Notes

  1. Note: classical definition of reliability “the ability of an item to perform a required function under given conditions for a given time interval” (NF EN 13306 2010) is not retained here. Actually, the acception used in this paper is according to application of machine learning approaches in PHM, that do not consider reliability as dependability measure (Bosnić and Kononenko 2009).

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Acknowledgments

This work was carried out within the Laboratory of Excellence ACTION funded by the French Government through the program “Investments for the future” managed by the National Agency for Research (ANR-11-LABX-01-01).

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Authors and Affiliations

  1. FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBM, 24 Rue Alain Savary, 25000, Besançon, France

    Kamran Javed, Rafael Gouriveau & Noureddine Zerhouni

  2. Singapore Institute of Manufacturing Technology, 71 Nayang Drive, 638075, Singapore, Singapore

    Xiang Li

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  1. Kamran Javed
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  2. Rafael Gouriveau
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  3. Xiang Li
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  4. Noureddine Zerhouni
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Correspondence to Kamran Javed.

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Javed, K., Gouriveau, R., Li, X. et al. Tool wear monitoring and prognostics challenges: a comparison of connectionist methods toward an adaptive ensemble model. J Intell Manuf 29, 1873–1890 (2018). https://doi.org/10.1007/s10845-016-1221-2

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  • DOI: https://doi.org/10.1007/s10845-016-1221-2

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