Research on Electrospindle Vibration Prediction Model Based on Improved Whale Algorithm to Optimize Convolutional Neural Network

During the high-speed operation of the mechanical electric spindle, due to the uneven distribution of rotor mass and long-term wear, the rotation deviates from the axis to produce vibration, resulting in low working accuracy, so the dynamic balance method is often used to reduce the impact caused by such reasons. Aiming at the problem that it is difficult for traditional electromechanical spindles to accurately adjust the vibration amplitude of spindle rotor in a short time in a long-term complex changing environment, a mechatronic spindle vibration prediction model based on the improved whale optimization algorithm (WOA) optimization convolutional neural network (CNN) is proposed. Experimental results show that the prediction model has higher accuracy for predicting electrospindle vibration, and the convergence speed is faster and more accurate.