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Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process

Abstract

Tool wear detection is a key issue for tool condition monitoring. The maximization of useful tool life is frequently related with the optimization of machining processes. This paper presents two model-based approaches for tool wear monitoring on the basis of neuro-fuzzy techniques. The use of a neuro-fuzzy hybridization to design a tool wear monitoring system is aiming at exploiting the synergy of neural networks and fuzzy logic, by combining human reasoning with learning and connectionist structure. The turning process that is a well-known machining process is selected for this case study. A four-input (i.e., time, cutting forces, vibrations and acoustic emissions signals) single-output (tool wear rate) model is designed and implemented on the basis of three neuro-fuzzy approaches (inductive, transductive and evolving neuro-fuzzy systems). The tool wear model is then used for monitoring the turning process. The comparative study demonstrates that the transductive neuro-fuzzy model provides better error-based performance indices for detecting tool wear than the inductive neuro-fuzzy model and than the evolving neuro-fuzzy model.

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Correspondence to Agustin Gajate.

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Gajate, A., Haber, R., del Toro, R. et al. Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process. J Intell Manuf 23, 869–882 (2012). https://doi.org/10.1007/s10845-010-0443-y

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  • DOI: https://doi.org/10.1007/s10845-010-0443-y

Keywords

  • Tool wear
  • Turning processes
  • Monitoring
  • Neuro-fuzzy inference system
  • Transductive reasoning