AN UNSUPERVISED NEURAL NETWORK APPROACH TO TOOL WEAR IDENTIFICATION

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Abstract
An unsupervised neural network approach is proposed for tool wear identification. Conventional pattern recognition approaches to automating the wear monitoring task are non-adaptive and require expensive or inaccessible information. Rangwala's application of the supervised backpropagation neural network to tool wear identification in a turning operation represented a pioneering effort to integrate sensor signals (cutting force and acoustic emission) and to employ a neural network in the classification of those signals. However, backpropagation also requires expensive training information and cannot remain adaptive after training. The unsupervised adaptive resonance network exhibited the ability to classify sensor signals into fresh and worn classes, to remain adaptive, and to utilize considerably less training information.