基于粒子群优化算法的电弧增材制造焊道尺寸反向传播神经网络预测模型

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

选取焊接电流、送丝速度、焊接速度及基板温度作为输入变量,焊道熔宽和余高作为输出变量,选择粒子群优化(PSO)算法中的最优粒子惯性权重和学习因子,构建熔化极惰性气体保护电弧增材制造316L不锈钢PSO反向传播(PSO-BP)神经网络模型。结果表明:PSO-BP神经网络模型预测的焊道熔宽与期望值的均方根误差、最大相对误差与平均相对误差分别为0.386,13.477%,2.580%,焊道余高的分别为0.152,10.372%,2.810%;相较于BP神经网络模型,PSOBP神经网络模型对焊道尺寸的预测精度更高,稳定性更强。

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.