Abstract
This paper presents tool wear estimation in face milling operations using the resource allocation network (RAN). Acoustic emission (AE) signals, surface roughness parameters and cutting conditions (cutting speed, feed) have been used to formulate input patterns. The performance of RAN has been compared with the multi-layer perceptron (MLP) trained using back-propagation (BP) algorithm, and the results are presented.
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