Process effect prediction model for EDM of engineering ceramics with neural network based on particle swarm optimization
                                                                               WANG He1, YANG Yong2
(1.Mechanical Engineering Department, Henan University of Engineering, Zhengzhou 451191, China;2.Shenyang Machine Tool (Dongwan) Intelligent Equipment Co., Ltd., Dongwan 523808, China)

Abstract: Aiming at the problem that there is a highly nonlinear relationship between the process electrical parameters and processing effect of the workpiece in electrical discharge machining (EDM) of engineering ceramics, and it is difficult to establish an accurate mathematical model, BP neural network model was established to predict the process effect of EDM of engineering ceramics, and particle swarm optimization algorithm with adaptive position variation was used to optimize the threshold and connection weights of the network model, which solved the problem that the BP neural network algorithm has a slow iteration speed and is easy to fall into the local optimal solution. Boron carbide was taken as an example, the algorithm was realized and the processing effect of the workpiece was predicted. The results indicate that the neural network algorithm based on the optimization of particle swarm optimization with adaptive position mutation can better reflect the nonlinear relationship between electrical parameters and surface roughness. The iteration times of the algorithm are significantly reduced, and the prediction accuracy is high. The reliability and validity of the model are confirmed.

Key words: engineering ceramic; electrical discharge machining(EDM); neural network; particle swarm optimization; adaptive position mutation