基于超球面支持向量机的刀具磨损状态识别

摘要

提出一种基于超球面支持向量机的刀具磨损状态识别方法.该方法提取切削力与振动信号中的多项特征,对各项特征分别进行刀具磨损量相关性分析,选择与刀具磨损变化量最相关的均值、均方根、小波系数能量以及小波系数近似熵组成特征向量.采用超球面支持向量机作为分类器,实现了刀具磨损状态的自动识别.实验证明,在小样本学习情况下,基于超球面支持向量机的刀具磨损状态识别方法具有良好的学习和泛化能力,获得较高的识别正确率.%New tool wear state recognition method based on hyper-sphere support vector machines was proposed. The correlation between the tool wear loss and the features acquired from cutting force and vibration signals of different wear states was analyzed. The mean value, mean square root, the energy and approximate entropy of wavelet coefficient were calculated and integrated as the feature vectors. Ultimately, in order to realize recognition of different wear states, hyper-sphere support vector machines (SVMs) algorithm was adopted as classifier. The results show that hyper-sphere SVMs are with excellent study ability, generalization ability and of high recognized precision with small training samples.