基于空间坐标与振动特征融合的机床切削状态分类方法

Thet-distributed stochastic neighbor embedding (t-SNE) is often used as a feature selection method in machine tool cutting state classification to learn potential relations among cutting parameters. Here, to improve I he accuracy of cutting state classification, a spatial coordinate embedded t-SNE (Ct-SNE) method was proposed to fuse vibration signal features and spatial coordinates of cutting excitation points. In this method, vibration signals were used to construct a high-dimensional feature space, and spatial coordinates were embedded into the feature space as physical information to select features with high in-class similarity and large inter-class difference. Processing data of a three-axis vertical milling machine in testswere collected, and visualization results and accuracies of cutting state classification of traditional t-SNE methodand Ct-SNE method were compared. The results showed that compared with the traditional method, introducing spatial coordinates of culling excitation points can improve the distinguishability of vibration features, and significantly improve the accuracy of cutting state classification.