Research and Development on Intelligent Cutting Tools with Capability of On-line Monitoring and Prediction

Abstract

Cutting-tool wear would affect machining accuracy, machining efficiency and machining cost. The on-line monitoring for cutting-tool wear would help monitor running state of cutting-tool in real time, handle accidents in time, reduce scrap occurrence during machining and cut down the cost caused by cutting-tool breakage. By the way of experimental research, wavelet analysis, neural network and grey theory, this article established a prediction model based on grey theory and wavelet neural network, developed an intelligent cutting-tool system with on-line monitoring and prediction, and which realize the real-time monitoring of the cutting-tool wear state and could help predict the abnormal condition of the cutting-tool in time. This system has some certain promotion significance.