Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network

On-line tool wear estimation plays a very critical role in industry automat ion for higher productivity and product quality. in addition, appropriate a nd timely decision for tool change is significantly required in the machini ng systems. Thus, this paper is dedicated to develop an estimation system t hrough integration of two promising technologies, artificial neural network s (ANN) and fuzzy logic. An on-line estimation system consisting of five co mponents: (1) data collection; (2) feature extraction; (3) pattern recognit ion; (4) multi-sensor integration; and (5) tool/work distance compensation for tool flank wear, is proposed herein. For each sensor, a radial basis fu nction (RBF) network is employed to recognize the extracted features. There after, the decisions from multiple sensors are integrated through a propose d fuzzy neural network (FNN) model. Such a model is self-organizing and sel f-adjusting, and is able to learn from the experience. Physical experiments for the metal cutting process are implemented to evaluate the proposed sys tem. The results show that the proposed system can significantly increase t he accuracy of the product profile. (C) 1999 Elsevier Science Ltd. All righ ts reserved.