Multilevel Classification of Milling Tool Wear with Confidence Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
AI approaches for cutting tool diagnosis in machining processes
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Clustering of time series data-a survey
Pattern Recognition
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
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Vibrations produced by the use of industrial machine tools can contain valuable information about the state of wear of tool cutting edges. However, extracting this information automatically is quite difficult. It has been observed that certain structures present in the vibration patterns are correlated with dullness. We present an approach to extracting features present in these structures using self-organizing feature maps (SOFMs). We have modified the SOFM algorithm in order to improve its generalization abilities and to allow it to better serve as a preprocessor for a hidden Markov model (HMM) classifier. We also discuss the challenge of determining which classes exist in the machining application and introduce an algorithm for automatic clustering of time-sequence patterns using the HMM. We show the success of this algorithm in finding clusters that are beneficial to the machine-monitoring application