Fundamentals of speech recognition
Fundamentals of speech recognition
Statistical Models in S
Automated tool condition monitoring in machining using fuzzy neural networks
Automated tool condition monitoring in machining using fuzzy neural networks
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
Self-organizing feature maps and hidden Markov models formachine-tool monitoring
IEEE Transactions on Signal Processing
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
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An important problem during industrial machining operations is the detection and classification of tool wear. Past research in this area has demonstrated the effectiveness of various feature sets and binary classifiers. Here, the goal is to develop a classifier which makes use of the dynamic characteristics of tool wear in a metal milling application and which replaces the standard binary classification result with two outputs: a prediction of the wear level (quantized) and a gradient measure that is the posterior probability (or confidence) that the tool is worn given the observed feature sequence. The classifier tracks the dynamics of sensor data within a single cutting pass as well as the evolution of wear from sharp to dull. Different alternatives to parameter estimation with sparsely-labeled training data are proposed and evaluated. We achieve high accuracy across changing cutting conditions, even with a limited feature set drawn from a single sensor.