Rj. Kuo et Ph. Cohen, Multi-sensor integration for on-line tool wear estimation through radial basis function networks and fuzzy neural network, NEURAL NETW, 12(2), 1999, pp. 355-370
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.