In this study, to accurately predict the temperature and melting ratio at low time and cost, the process of dissimilar laser welding of stainless steel 304 and copper was simulated based on artificial neural network (ANN). Among various ANN models, the Bayesian regulation backpropagation training method was utilized to model the current problem. This method was used considering the two temperatures of copper and steel and the two melting ratios of steel and copper as the four outputs, and the four parameters, pulse width, pulse frequency, welding speed, and focal length, as the inputs. According to the results, regression values had a good accuracy in all cases and the histogram diagrams indicated that the error distribution was mainly concentrated at the center; in other words, the major errors of the network were not very large. It was also observed that the error concerning the trained neural networks was acceptable in the experiment phase. Finally, this neural network could be used as a numerical model to estimate the four outputs of steel temperature, copper temperature, steel melting ratio, and copper melting ratio for all input values of pulse width, pulse frequency, welding speed, and focal length in the studied range, without any need to rerun the experiment.

中文翻译：

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基于人工神经网络模型的不锈钢304与铜异种激光焊接研究

在本研究中，为了以较低的时间和成本准确预测温度和熔化率，基于人工神经网络 (ANN) 模拟了 304 不锈钢和铜的异种激光焊接过程。在各种人工神经网络模型中，利用贝叶斯规则反向传播训练方法对当前问题进行建模。该方法以铜和钢的两个温度和钢和铜的两个熔化比作为四个输出，脉冲宽度、脉冲频率、焊接速度和焦距四个参数作为输入。结果表明，回归值在所有情况下都具有良好的准确性，直方图表明误差分布主要集中在中心；换句话说，网络的主要误差不是很大。还观察到有关训练神经网络的误差在实验阶段是可以接受的。最后，这个神经网络可以作为一个数值模型，在脉冲宽度、脉冲频率、焊接速度和焦距的所有输入值下，估计钢温度、铜温度、钢熔化比和铜熔化比这四个输出。研究范围，无需重新运行实验。