Multilevel Classification of Milling Tool Wear with Confidence Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hidden Markov models for monitoring machining tool-wear
ICASSP '00 Proceedings of the Acoustics, Speech, and Signal Processing, 2000. on IEEE International Conference - Volume 06
Tool-Wear monitoring based on continuous hidden markov models
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Self-organizing feature maps and hidden Markov models formachine-tool monitoring
IEEE Transactions on Signal Processing
Real-time tool condition monitoring using wavelet transforms and fuzzy techniques
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Fusion of hard and soft computing techniques in indirect, online tool wear monitoring
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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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.