Tool wear estimation in micro-machining. Part II: neural-network-based periodic inspector for non- metals

Cutting forces are small, and in many cases insignificant, compared with noise during the micro-machining of many non-metals. The Neural-Network-based Periodic Tool Inspector (NPTI) is introduced to evaluate tool condition periodically on a test piece during the machining of non-metal workpieces. The cutting forces are measured when a slot is being cut on the test piece and the neural network estimates the tool life from the variation of the feedand thrust-direction cutting forces. The performances of three encoding methods (force variation, segmental averaging and wavelet transformations) and two neural networks [backpropagation (BP) and probabilistic neural network (PNN)] are compared. The advantages of NPTI are simplicity, low cost, reliability and simple computational requirements.  1999 Elsevier Science Ltd. All rights reserved.