Backpropagation Neural Network Prediction Model of Arc Additive Manufacturing Weld Size Base on Particle Swarm Optimization Algorithm

With welding current, wire feed speed, welding speed and substrate temperature as input variables, weld width and residual height as output variables, and the 4-12-2 structure particle swarm optimization backpropagation（PSO-BP）neural network model of melt intert-gas welding arc additive manufacturing 316L stainess steel was built with optimal particle inertia weight and learning factor in PSO algorithm. The results show that the root-mean-square error, maximum relative error and average relative error of predicted weld width obtained by PSO-BP neural network model and expected values were 0.386, 13.477% and 2.580%, and those of weld reinforcement were 0.152, 10.372% and 2.810%, respectively. Compared with BP neural network model, PSO-BP neural network model had higher prediction accuracy and stronger stability for the prediction of weld size.