Deep Spatial-Temporal Feature Extraction and Lightweight Feature Fusion for Tool Condition Monitoring

Tool condition monitoring (TCM) is vital to maintain the quality of workpieces during machining. Recently, data-driven methods based on multisensory data have been applied to TCM. The quality of extracted features is a key to realizing a successful data-driven TCM. However, the extracted features in the previous study are focused on the multicollinearity of multisensory data, which is incapable of identifying the informative and discriminative information in the long time period aspect. This article proposed a novel method for TCM using deep spatial-temporal feature extraction and lightweight feature fusion techniques. A key to the proposed method is the extraction of multicollinearity as spatial features (SPs), and the capture of long-range dependencies and nonlinear dynamics as temporal features (TFs), to fully characterize tool wear change using multisensory data. Then, a lightweight feature fusion method is used to fuse SPs, TFs, and statistical features for further removing redundant information employing the kernel-principal component analysis. Finally, support vector machines is used to predict the tool conditions using the fusion feature. Experiments on a milling machine and a gear hobbing machine are carried out to verify the effectiveness and generalization of the proposed method respectively.