AI approaches for cutting tool diagnosis in machining processes
Monitoring of cutting tool systems are very important in machine tools and manufacturing equipment due to the impact they have in quality products and economy production. The cutting tool condition can be determined by direct or indirect sensing methods. Indirect methods are the only practical approach that offers better results by exploiting data sensor fusion techniques, which help to make a more robust and stable diagnosis. Different successful approaches from the Artificial Intelligence (AI) community are reviewed. A discussion of the implementation and evaluation of two AI techniques is done. Hidden Markov Model (HMM) based and Bayesian Networks based into an industrial machining center are tested. Excellent results demonstrated that HMM-based approach has a potential industrial application.
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