Prediction of Tool Life in Digital Workshop Based on Particle Swarm Optimized BP Neural Network Algorithm

Xiaoqiang Wu, Chunyou Zhang, Lihua Wang


In the machining process of modern digital workshops, the tool life is an important parameter index that influences the development of tool demand planning, cost accounting, and cutting parameter setting. In view of the highly nonlinear relationship between tool life factors and tool life, particle swarm optimization BP neural network technology was used to predict tool life. Firstly, the relationship between tool life and tool wear, the influence factors of tool life and the significance of tool life prediction are analyzed. Then, a tool life prediction algorithm based on particle swarm optimization BP neural network was established. Finally, the proposed algorithm is simulated and tested. Experimental results show that compared with the original BP neural network prediction algorithm, the proposed algorithm has fast convergence rate and high prediction accuracy.


Particle Swarm Optimization, Bp Neural Network, Tool Life, Prediction


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