基于Elman网络算法的Inconel625合金堆焊稀释率的控制

Control of dilution rate of Inconel 625 alloy surfacing based on elman network algorithm

文中建立了5-8-3结构的反馈Elman神经网络模型,以电弧长度、焊接电流、焊接速度、送丝速度和保护气流量为输入量,堆焊焊缝的熔宽、熔高和稀释率为输出量进行堆焊仿真计算分析.计算结果表明,Elman模型的预测结果比BP和GRNN神经网络更精确.建立了以电弧长度(X)、堆焊电流(Y)和送丝速度(Z)为空间坐标,堆焊稀释率δ等于f(X,Y,Z)为目标函数的四维图像来确定δ≤5%的堆焊工艺窗口.分别进行Elman模型仿真计算和堆焊工艺试验,得到的稀释率δ分别为2.55%和3.32%,仿真计算的稀释率的相对误差约0.8%,证实了Elman模型预测的Inconel625合金堆焊工艺窗口的可行性与可靠性.

This paper established the feedback Elman neural network model of a 5-8-3 structure,in order to analysis the correlation between the input parameters of surfacing welding process including the arc length, welding current, welding speed,wire feeding speed and shielding gas flow rate and these output variables such as the height,the widththe dilution rate of the surfacing weld. The result showed that the Elman model prediction results were more accurate than BP and GRNN neural network. A four dimensional image to determine surfacing welding process window with error bound of δ≤5% was established on the basis of simulation calculation results of the Elman model,in which arc length( X),welding current( Y) and wire feed speed( Z) as the space coordinates and surfacing welding dilution rate function δ = f( X,Y,Z) as the objective function. The dilution rate δ were 2. 55% and 3. 32% respectively,the relative error of dilution rate of the simulation was about 0. 8%,which confirmed the feasibility and reliability of the Elman model predictions.