1. Mechanical College, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Heavy Intelligent Manufacturing Equipment Technology Innovation Center of Hebei Province, Qinhuangdao, Hebei 066004, China
Abstract：Chatter in ultrasonic milling of thin-walled parts seriously affects the quality of workpiece and aggravates tool wear, so a chatter image monitoring system was built. Convolutional neural network (CNN) was used to identify chatter images and the advantages of magnetotactic bacteria algorithm (MB), hill climbing algorithm (HC) and tabu search algorithm (TS) were taken synthetically to improve MB algorithm for optimizing the parameters. Therefore, a chatter identification method based on the improved magnetic bacteria convolution neural network (IMB-CNN) for ultrasonic milling of thin-walled parts was proposed. Firstly, the global search was carried out by MB algorithm, and then neighborhood search was carried out by HC algorithm with the optimal solution as the initial point, so as to avoid the oscillation of MB algorithm near the optimal solution. At the same time, the tabu list was used to skip the searched nodes to reduce the calculation scale and speed up the calculation efficiency. Finally, the optimal hyperparameters were applied to the CNN to realize the accurate identification of flutter images. Compared with other methods, this method achieves 97.69% recognition rate, and the judgment time is 363ms. The chatter is identified effectively, and the overall performance is better than other algorithms.
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2022, Vol. 43 
