|
Abstract: A
new methodology of tool wear classification based on dynamic tree is
proposed. The correlation coefficients approach is utilized to extract
several features with a close relation to tool wear. B-spline neural
networks charactered by local memory is introduced to establish the
nonlinearity relation between tool wear amounts and monitoring features
extracted from acoustic emission，dynamometer and vibration sensors. Tool
wear monitoring systems is so built under arbitrary machining
conditions, and the integrated neural networks give the final
classifying results of tool wear. The experimental results indicate that
the tool wear monitoring system founded on the methodology is provided
with high precision，high reliability，good multiplication and rapid
recognizing speed，so it is good for popularization in industry．
Key words:Tool
wear Dynamic tree B-spline Fuzzy neural network Integrated
neural network
CLC No: TH164
Received 20050806, received in
revised form 20060115
|