水下高压干式GMAW焊缝成形难以控制，影响焊接质量，通过多因素权重法对影响高压GMAW焊缝成形的参数进行分析，以确定焊接过程中主要参数与焊缝成形之间的关系. 以高压GMAW焊接正交试验结果作为训练样本，利用BP神经网络建立了焊缝成形预测模型. 将模型预测结果与实际焊接试验结果进行对比，验证了模型的精确度. 以预测模型中的权值为基础，通过Garson算法得出各参数变量对于焊缝成形参数的权重系数，并通过高压干式GMAW试验进行验证. 结果表明，不同焊接参数变化引起的焊缝成形参数变化幅度与相应焊接参数对焊缝成形的影响权重基本对应，所得权重系数对于高压GMAW焊接工程应用具有指导作用.
A multi-factor weighting method was used to analyze the parameters influencing weld bead geometry of hyperbaric GMAW. The orthogonal test results of hyperbaric GMAW were taken as training samples and the prediction model for weld bead geometry was established via BP neural network. Accuracy of the model was verified by comparing the prediction results with the actual welding test results. Based on the weights and thresholds in the prediction model, the weighting coefficient of each factor to the parameters of weld bead geometry was obtained by the Garson algorithm, which was verified by experiments. The results show that the weighting coefficients obtained by the multi-factor weighting method were consistent with the variation tendencies of the parameters of weld bead geometry with various factors and the weighting coefficients had a guiding role for the engineering application of hyperbaric GMAW.
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