Tool wear monitoring using neuro-fuzzy techniques: a comparative study in a turning process
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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.
KeywordsTool wear Turning processes Monitoring Neuro-fuzzy inference system Transductive reasoning
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- Gajate, A., Haber, R. E., Alique, J. R., & Vega, P. I. (2009). Transductive-weighted neuro-fuzzy inference system for tool wear prediction in a turning process. Lecture Notes in Artificial Intelligence (Vol. 5572, pp. 113–120).Google Scholar
- Pal, S., Heyns, P. S., Freyer, B. H., Theron, N. J., & Pal, S. K. (2009). Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties. Journal of Intelligent Manufacturing (in press).Google Scholar
- Purushothaman, S. (2009). Tool wear monitoring using artificial neural network based on extended Kalman filter weight updation with transformed input patterns. Journal of Intelligent Manufacturing (in press).Google Scholar
- Song, Q., & Kasasbov, N. (2001). ECM, a novel on-line, evolving clustering method and its applications. In Proceedings of 5th Biannu Conf Artif Neural Netw Expert Syst—ANNES 2001 (pp 87–92).Google Scholar