Gas tungsten arc welding (GTAW) is the primary joining process for critical applications where joining precision is crucial. However, variations in manufacturing conditions adversely affect the joining precision. The dynamic joining process needs to be monitored and adaptively controlled to assure the specified weld quality be produced despite variations. Among required weld qualities, the weld joint penetration is often the most critical one as an incomplete penetration causes explosion under high temperature/pressure and an excessive penetration/heat input affects the flow of fluids and degrades materials properties. Unfortunately, detecting its development, how the melted metal has developed within the work-piece, is challenging as it occurs underneath and is not directly observable. The key to solving the problem is to find, or design, measurable physical phenomena that are fully determined by the weld penetration and then correlate the phenomena to the penetration. Analysis shows that the weld pool surface that is directly observable using an innovative active vision method developed at the University of Kentucky is correlated to the thermal expansion of melted metal, thus the weld penetration. However, the surface is also affected by prior conditions. As such, we propose to form a composite image from the image taken from the initial pool, reflecting prior condition and from real-time developing pool such that this single composite image reflecting the measurable phenomena is only determined by the development of the weld penetration. To further correlate the measurable phenomena to the weld penetration, conventional methods analyze the date/images and propose features that may fundamentally characterize the phenomena. This kind of hand engineering method is tedious and does not assure success. To address this challenge, a convolutional neural network (CNN) is adopted that allows the raw composite images to be used directly as the input without need for hand engineering to manually analyze the features. The CNN model is applied to train, verify and test the datasets and the generated training model is used to identify the penetration states such that the welding current can be reduced from the peak to the base level after the desired penetration state is achieved despite manufacturing condition variations. The results show that the accuracy of the CNN model is approximately 97.5%.

气体钨极电弧焊（GTAW）是关键应用的主要连接工艺，在这些应用中，连接精度至关重要。但是，制造条件的变化会不利地影响连接精度。动态连接过程需要进行监控和自适应控制，以确保即使出现变化也可以产生指定的焊接质量。在所需的焊接质量中，焊缝熔深通常是最关键的一种，因为不完全熔深会在高温/高压下引起爆炸，而过多的熔深/热输入会影响流体流动并降低材料性能。不幸的是，检测熔融金属在工件内部的发展情况是困难的，因为熔融金属发生在下方且无法直接观察到。解决问题的关键是寻找或设计 由焊缝熔深完全确定的可测量物理现象，然后将这些现象与熔深相关联。分析表明，使用肯塔基大学开发的创新主动视觉方法可以直接观察到的焊缝表面与熔融金属的热膨胀相关，从而与焊缝熔深相关。但是，该表面也受先前条件的影响。因此，我们建议从初始池中获取的，反映先验条件的图像和实时显影池中的图像形成合成图像，以使反映可测量现象的单个合成图像仅由焊缝熔深的发展确定。为了进一步将可测量现象与焊缝熔深相关联，传统方法分析日期/图像并提出可以从根本上表征现象的特征。这种手工工程方法很繁琐，并且不能保证成功。为了应对这一挑战，采用了卷积神经网络（CNN），该神经卷积神经网络可将原始合成图像直接用作输入，而无需手动工程来手动分析特征。CNN模型用于训练，验证和测试数据集，生成的训练模型用于识别熔深状态，以便在达到所需熔深状态后，尽管有制造条件，也可以将焊接电流从峰值降低到基本水平变化。结果表明，CNN模型的准确性约为97.5％。这种手工工程方法很繁琐，并且不能保证成功。为了应对这一挑战，采用了卷积神经网络（CNN），该神经卷积神经网络可将原始合成图像直接用作输入，而无需手动工程来手动分析特征。CNN模型用于训练，验证和测试数据集，生成的训练模型用于识别熔深状态，以便在达到所需熔深状态后，尽管有制造条件，也可以将焊接电流从峰值降低到基本水平变化。结果表明，CNN模型的准确性约为97.5％。这种手工工程方法很繁琐，并且不能保证成功。为了应对这一挑战，采用了卷积神经网络（CNN），该神经卷积神经网络可将原始合成图像直接用作输入，而无需手动工程来手动分析特征。CNN模型用于训练，验证和测试数据集，生成的训练模型用于识别熔深状态，以便在达到所需熔深状态后，尽管有制造条件，也可以将焊接电流从峰值降低到基本水平变化。结果表明，CNN模型的准确性约为97.5％。采用卷积神经网络（CNN），可将原始合成图像直接用作输入，而无需进行手工工程来手动分析特征。CNN模型用于训练，验证和测试数据集，生成的训练模型用于识别熔深状态，以便在达到所需熔深状态后，尽管有制造条件，也可以将焊接电流从峰值降低到基本水平变化。结果表明，CNN模型的准确性约为97.5％。采用了卷积神经网络（CNN），可将原始合成图像直接用作输入，而无需进行手工工程来手动分析特征。CNN模型用于训练，验证和测试数据集，生成的训练模型用于识别熔深状态，以便在达到所需熔深状态后，尽管有制造条件，也可以将焊接电流从峰值降低到基本水平变化。结果表明，CNN模型的准确性约为97.5％。验证和测试数据集，并使用生成的训练模型来识别熔深状态，以便尽管制造条件有所变化，在达到所需的熔深状态后，焊接电流仍可从峰值降低到基本水平。结果表明，CNN模型的准确性约为97.5％。验证和测试数据集，并使用生成的训练模型来识别熔深状态，以便尽管制造条件有所变化，在达到所需的熔深状态后，焊接电流仍可从峰值降低到基本水平。结果表明，CNN模型的准确性约为97.5％。