Journal of Intelligent Manufacturing

, Volume 23, Issue 3, pp 869–882 | Cite as

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

  • Agustin Gajate
  • Rodolfo Haber
  • Raul del Toro
  • Pastora Vega
  • Andres Bustillo
Article

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.

Keywords

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

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Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Agustin Gajate
    • 1
  • Rodolfo Haber
    • 1
  • Raul del Toro
    • 1
  • Pastora Vega
    • 2
  • Andres Bustillo
    • 3
  1. 1.Institute of Industrial AutomationSpanish Council for Scientific Research (CSIC)MadridSpain
  2. 2.Department of Informatics and AutomationUniversity of SalamancaSalamancaSpain
  3. 3.Department of Applied Computational IntelligenceUniversity of BurgosBurgosSpain

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