关键词: 切削过程功率, 刀具磨损, 麻雀搜索算法, 长短时记忆神经网络, 变分模态分解, 计算机视觉技术

Abstract: Traditional researches of cutting process powers required complex cutting power models and often neglected the influences of tool wear, so a CNC milling machine cutting power prediction model considering tool wear was designed based on VMD, SSA, and LSTM neural network. This model did not require the deconstruction of the energy consumption mechanism during the operation of CNC milling machines, and achieved high-precision prediction of cutting process powers based on historical experimental data. Firstly, artificial intelligence machine vision technology was used to analyze and process images of the tool wear, obtaining digital features of the worn tools and determining the maximum wear. Then, the VMD-SSA-LSTM model was established, which considered tool wear in the prediction of CNC milling machine cutting powers. VMD was used to decompose the operational data of CNC milling machines, and then the SSA algorithm optimized the hyperparameters of the LSTM neural network. The decomposed milling machine data components were input into the LSTM neural network, and the predicted values of each component were summed to obtain the cutting power prediction value. Taking face milling as an example, the proposed prediction model was compared and analyzed against BP neural networks, LSTM neural networks, and traditional models, which validated the effectiveness and superiority of the proposed model.

Key words: power of cutting process； tool wear； sparrow search algorithm(SSA), long-short term memory(LSTM) neural network； variational mode decomposition(VMD)； computer vision technology