New approach of milling state recognition based on attention mechanism weighted fusion K-nearest neighbor under framework of chaotic attractor using accelerated life signal

Abstract

The surface quality of the finished workpiece and the lifetime of the machining tool will be damaged and reduced due to the issue of untimely milling monitoring and identification. To alleviate this bottleneck, in this paper, a new milling state recognition approach named hybrid attention mechanism WFKNN model under the framework of chaotic attractor and deep convolutional neural network (CNN) is proposed during the milling process. Specifically, three milling stages, that is, early-term stage, mid-term stage, and ultimate-term milling stage, are divided through Duffing chaotic attractor (DCA) characteristic, where the periodicity and vorticity phenomena under different milling states will be revealed using DCA characteristic. Then, the signal features with respect to the milling condition are extracted by the CNN-attention mechanism strategy, and the extracted signal features will be fed into the improved KNN classifier optimized by improving the distance metric. Eventually, the availability and feasibility of the proposed approach is verified via six group run-to-failure datasets of the milling process. The results show that a higher identify accuracy is obtained via the proposed approach compared with existing benchmarks such as the CNN model.