Prediction of weld formation in 5083 aluminum alloy by twin-wire CMT welding based on deep learning
Prediction of weld formation in 5083 aluminum alloy by twin-wire CMT welding based on deep learning
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DOI：
10.1007/s40194-019-00726-z
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发表时间：
2019-06
影响因子：
2.1
通讯作者：
L. Yin;Jinzhao Wang;Huiqin Hu;Shanguo Han;Yupeng Zhang
中科院分区：
文献类型：
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作者：
L. Yin;Jinzhao Wang;Huiqin Hu;Shanguo Han;Yupeng Zhang
基于5083铝合金双丝CMT焊的大量试验数据，采用深度神经网络技术对焊接工艺参数和焊缝尺寸进行了分析，建立了焊缝成形参数的精确预测模型。结果表明，影响双线CMT深度神经网络模型预测精度的关键参数是隐层神经元数目、网络训练迭代次数和深度网络的学习速率。对于单因素，无论焊缝宽度、焊缝熔深或焊缝钢筋，预测值曲线变化平稳，与实测值曲线无变形。通过对预测数据和实际数据进行线性回归分析，可以评价复杂非线性模型的精度。此外，由于深度神经网络在弧焊系统从输入焊接参数到输出焊接尺寸的定量分析中具有强大的多维非线性拟合能力，因此具有明显的高效率和高精度的优势。该模型可为5083铝合金双丝CMT焊接或附加制造的工艺设计和热源尺寸的确定提供数据支持和科学参考。该模型为深度学习技术在焊接领域的应用提供了创新思路。
Based on a large amount of experimental data from twin-wire CMT welding of 5083 aluminum alloy, deep neural network technology was adopted to analyze the welding process parameters and the weld dimensions, and a precise prediction model for the weld formation parameters was established. The results show that the key parameters influencing the prediction accuracy of the twin-wire CMT deep neural network model are the number of hidden layer neurons, the number of network training iterations, and the learning rate of the deep network. For a single factor, regardless of the weld width, weld penetration or weld reinforcement, the predicted value curve changes smoothly and without distortion from the measured value curve. The accuracy of the complex nonlinear model can be evaluated by linear regression analyses of the predicted data and the measured data. In addition, the deep neural network has the obvious advantages of high efficiency and precision due to its strong multi-dimensional nonlinear fitting abilities in the quantitative analysis of the arc welding system from the input welding parameters to the output weld dimensions. This model can provide data support and scientific reference for the process designs for 5083 aluminum alloy twin-wire CMT welding or additive manufacturing and the determination of the numerically calculated heat source size. Also, this model provides innovative ideas for the application of deep learning technology in the welding field.