A Neural Network Approach to Tool Wear Monitoring in End Milling Operations

J.Y.H. Lam and A. Geddam (PRC)


End milling; Neural networks; Cutting forces; Tool wear


This paper outlines a methodology of continuously and indirectly monitoring tool wear in end milling operations. A neural network based computer control system was developed for monitoring the tool wear with machining variables such as spindle speed, feed rate, and depth of cut using measured cutting forces under a variety of cutting conditions in end milling operations. The supervised neural network developed was able to extract tool wear information from the changes occurring in the machining process. The results indicate that the neural networks approach presents an efficient and economic method for predicting the tool condition with the measured process variables. manufacturing environment based on the observation of sensor data with minimum human intervention [11]. In the research reported here, a computer-based control system which can predict the machining parameters based on sensor measurements of the machining process in end milling operations was investigated. The system developed uses a neural networks approach for monitoring tool wear using cutting force data measured under a variety of machining conditions [12]. Fig. 1 illustrates an experimental strategy of the investigation. The results presented indicate the neural networks approach for tool wear monitoring is a promising method.

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