Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals
Artificial neural network modeling of weld joint strength prediction of a pulsed metal inert gas welding process using arc signals
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DOI：
10.1016/j.jmatprotec.2007.09.039
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发表时间：
2008-06
影响因子：
6.3
通讯作者：
S. Pal;Surjya K. Pal;A. Samantaray
中科院分区：
文献类型：
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作者：
S. Pal;Surjya K. Pal;A. Samantaray
研究了脉冲熔化极惰性气体保护焊（PMIGW）焊接接头强度的监测问题.应用响应面法进行焊接试验。建立了一个多层神经网络模型，用于预测焊接板的极限拉应力。该模型以脉冲电压、背景电压、脉冲宽度、脉冲频率、送丝速度和焊接速度6个工艺参数以及焊接电流和电压的均方根值为输入变量，以被焊板的极限拉伸强度为输出变量。此外，通过多元回归分析得到的输出是用来比较与发达的人工神经网络（ANN）模型的输出。结果表明，所建立的神经网络模型预测的焊接强度优于多元回归分析。
This paper addresses the weld joint strength monitoring in pulsed metal inert gas welding (PMIGW) process. Response surface methodology is applied to perform welding experiments. A multilayer neural network model has been developed to predict the ultimate tensile stress (UTS) of welded plates. Six process parameters, namely pulse voltage, back-ground voltage, pulse duration, pulse frequency, wire feed rate and the welding speed, and the two measurements, namely root mean square (RMS) values of welding current and voltage, are used as input variables of the model and the UTS of the welded plate is considered as the output variable. Furthermore, output obtained through multiple regression analysis is used to compare with the developed artificial neural network (ANN) model output. It was found that the welding strength predicted by the developed ANN model is better than that based on multiple regression analysis.