Tool Wear Monitoring Using FNN with Compact Support Gaussian Function

摘要

A novel approach of tool wear monitoring based on localized fuzzy neural networks with compact support Gaussian basis function (CSGFFNN) was proposed to improve classification accuracy of tool states and solve the problems of slow computing speed of BP neural networks. By analyzing cutting forces signals, acoustic emission signals and vibration signals in time domain, frequency domain, and time-frequency domain, a series of features that sensitive to tool states were selected as inputs of neural networks according to synthesis coefficient. The nonlinear relations between tool wear and features were modeled by using CSGFFNN that constructed and optimized through fuzzy clustering and an adaptive learning algorithm. The experimental results show that the monitoring system based on CSGFFNN is provided with high precision, rapid computing speed and good multiplication.