Force parameters for on-line tool wear estimation: A neural network approach

Reliable on-line tool conditioning monitoring is an essential feature of modern sophisticated and automated machine tools. Appropriate and timely decision for tool-change is urgently required in the machining systems. Ample researches have been carried out in this direction. Recently artificial neural networks (NN) are applied for this purpose in conjunction with suitable sensory systems. Its fast processing capability is well-suited for quick estimation of tool condition and corrective measure to be taken. The present work uses back-propagation type training and feed-forward testing procedures for the neural networks. Three models using different force parameters are tried to monitor tool wear on-line. The close estimation of the modeled output to the actual wear value demonstrates the possibility of successful tool wear monitoring. Copyright (C) 1996 Elsevier Science Ltd.