Journal of Intelligent Manufacturing

, Volume 29, Issue 8, pp 1873–1890 | Cite as

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

  • Kamran Javed
  • Rafael Gouriveau
  • Xiang Li
  • Noureddine Zerhouni


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.


Applicability Data-driven Ensemble Monitoring Prognostics Robustness Reliability 



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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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kamran Javed
    • 1
  • Rafael Gouriveau
    • 1
  • Xiang Li
    • 2
  • Noureddine Zerhouni
    • 1
  1. 1.FEMTO-ST Institute (AS2M Department), UMR CNRS 6174, UBFC/ UFC/ ENSMM / UTBMBesançonFrance
  2. 2.Singapore Institute of Manufacturing TechnologySingaporeSingapore

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