Do We Need a New Foundation to Use Deep Learning to Monitor Weld Penetration?
Do We Need a New Foundation to Use Deep Learning to Monitor Weld Penetration?
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DOI：
10.1109/lra.2023.3270038
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发表时间：
2023-06
影响因子：
5.2
通讯作者：
Edison Mucllari;Rui Yu;Yue Cao;Qiang Ye;Yuming Zhang
中科院分区：
文献类型：
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作者：
Edison Mucllari;Rui Yu;Yue Cao;Qiang Ye;Yuming Zhang
深度学习已成功用于自动化建模过程，从给定的实验数据集训练网络/模型，以使用高维复杂原始数据直接计算输出。然而，经过训练的网络是焊接过程（正向过程）的逆过程，它产生焊接现象/测量的原始数据作为输出，而熔深作为正向过程的输入。现在的问题是，除了要估计的焊接熔深的当前状态之外，前向过程是否还有其他输入来确定其输出。如果有，则必须相应地构建逆模型。这需要为基于深度学习的渗透监测奠定新的基础。这封信提出了一种新颖的创新生成对抗网络（GAN），生成器中带有 GRU（门控循环单元），即 GRU-GAN，对极其复杂的前向过程进行建模，从背面图像（作为焊接熔深的综合量化）生成观察到的正面焊接图像（前向过程的输出）。研究发现，生成的正面焊接图像不仅由当前背面图像决定，还由其历史记录决定。因此，必须建立一个新的基础来指导基于深度学习的焊缝熔深监控。作为逆模型的预测模型/网络必须符合正向过程，其中包括焊缝熔深状态的历史作为其输入。
Deep learning has been successfully used to automate the modeling process that trains a network/model from a given experimental dataset to calculate the output directly using high-dimensional complex raw data. However, the trained network is an inverse of the welding process (forward process) that produces the welding phenomena/measured raw data as the output with the penetration as the input of the forward process. Now the question is in addition to the current state of the weld penetration to be estimated if the forward process also has other inputs to determine its output. If it has, then the inverse model has to be constructed accordingly. This will call for a new foundation for deep learning-based monitoring of penetration. This letter proposed a novel innovative generative adversarial network (GAN) with GRU (Gated Recurrent Unit) in the generator, i.e., GRU-GAN, to model the extremely complex forward process to generate the observed topside welding image (output of the forward process) from the backside images (as comprehensive quantification of weld penetration). It is found that the produced topside welding image is not only determined by the current backside image but also by its history. A new foundation thus must be established to guide deep learning-based monitoring of weld penetration. The prediction model/network as an inverse model must be in compliance with the forward process that includes the history of the state of the weld penetration as its input.