基于计算智能算法的铣刀状态监测

End Mill Wear Monitoring Based on Computational Intelligent Algorithms

本文提出了基于智能融合技术进行铣刀磨损量监测和预测方法。利用多传感器对切削力和振动信号进行监测,通过频率变换提取切削力特征量,采用小波包分解技术提取振动信号特征量。通过信号特征值的组合,分别探讨了几种计算智能数据融合技术-小波神经网络、遗传神经网络、遗传小波神经网络对刀具磨损量的预测效果。实验分析表明,本文提出的几种计算智能数据融合技术均能够有效地完成刀具磨损量预测。

A computational intelligent data fusion method for monitoring end mill wear is presented in this paper. The signals of cutting force and vibration are measured with multi-sensors, the cutting force features are extracted with frequency mutation and the vibration features are extracted using wavelet package decomposition. Several computational intelligent data fusion methods, which are wavelet neural networks, genetic algorithm neural networks （GA-NN）, and wavelet generic algorithm neural networks for predicting the tool wear values are discussed. The experimental results show all of these presented methods can effectively perform tool wear prediction.