Deep learning based real-time and in-situ monitoring of weld penetration: Where we are and what are needed revolutionary solutions?
Deep learning based real-time and in-situ monitoring of weld penetration: Where we are and what are needed revolutionary solutions?
复制标题
DOI：
10.1016/j.jmapro.2023.03.011
复制
发表时间：
2023-05
影响因子：
6.2
通讯作者：
Rui Yu;Yue Cao;Heping Chen;Qiang Ye;Yuming Zhang
中科院分区：
文献类型：
--
作者：
Rui Yu;Yue Cao;Heping Chen;Qiang Ye;Yuming Zhang
设计的焊接程序确保在额定焊接条件下产生所需的焊接熔深。当条件偏离标称条件时，熔深和其他焊接结果就会偏离期望的/目标的结果。为了确保渗透率不低于最低限度的必要性，设计良好的程序应根据估计的条件偏差保留适当的裕度。理想的解决方案是使用相对较小的余量，但动态调整焊接参数，以将熔深保持在最小必要的余量/在余量内。因此，应在制造过程中实时监控渗透状态。不幸的是，它发生在工件下面，在制造过程中不被认为是直接可见的，因此它的实时现场监测是具有挑战性的。在过去的半个世纪里，研究人员一直专注于寻找有希望的实时可观察现象，并将这些现象与渗透联系起来。这一直是困难的，因为不清楚在观察到的现象中什么是关键的，并且正在进行反复试验的迭代过程，提出特征，开发算法来计算它们，根据特征拟合模型，然后如果拟合精度不能接受，则修改模型、特征或算法。这样的迭代过程不是自动化的，寻找/匹配合适的特征/模型需要数月甚至更长的时间，并且不能保证成功。特别是，计算特征的算法因特征而异，每一种算法都需要广泛的测试。深度学习自动化并结合特征和适配，以最大限度地直接使用原始信息，实现最高的精度。计算量大大增加，但迭代过程被自动化的过程所取代，因此时间框架仍然大大缩短。本文回顾了被用作深度学习模型输入的观测现象的各种原始信息，并分析了它们可能与渗透相关的原因；回顾了各种深度学习模型，并分析了为什么/如何将不同的原始信息与渗透相关联；以及简要回顾了用于训练深度学习模型的渗透预测的主要技术。在这些分析的基础上，本文指出了迄今为止利用深度学习方法监测焊缝熔深所取得的主要成就和存在的问题。最后，我们确定了两个需要革命性解决方案的基本问题，以便将深度学习技术从实验室研究转移到制造，作为未来努力的方向。对这两个问题进行了分析，并提出了初步的解决方向。
Welding Procedure Designed assures the desired weld penetration be produced under nominal welding conditions. When conditions deviate from the nominal, penetration and other welding outcomes deviate from their desired/targeted ones. To assure the penetration not below minimally necessary, well-designed Procedure should reserve an appropriate margin per estimated condition deviations. An ideal solution is to use relatively small margin but dynamically adjust welding parameters to maintain the penetration at minimally necessary/within the margin. As such, the penetration state should be monitored in real-time during manufacturing. Unfortunately, it occurs underneath workpieces and is not considered directly observable during manufacturing so that its real-time in-situ monitoring is challenging. In the last half century, researchers have focused on finding promising real-time observable phenomena and correlating such phenomena to penetration. This has been difficult as it is unclear what are critical in observed phenomena and trial-and-error iterative processes are practiced proposing features, developing algorithms to calculate them, fitting model from features, and then modifying model, features, or algorithms if fitting accuracy is not acceptable. Such iterative process is not automated, finding/fitting right features/model takes months if not longer, and success is not assured. In particular, algorithms to calculate features vary from features to features and each of them requires extensive tests. Deep learning automates and combines featuring and fitting to maximally use the raw information directly to achieve highest accuracies. Computation is drastically increased but the iterative process is replaced by an automated one so that the time frame is still drastically reduced. This paper reviews various raw information that has been used as the observed phenomena to input into deep learning models and analyzes why they may correlate to penetration; reviews various deep learning models and analyzes why/how they may and are needed to correlate different raw information to penetration; and briefly reviews major techniques that have been used to train deep learning models in penetration prediction. Based on such analyses, this paper identifies major achievements and issues for efforts taken so far to monitor weld penetration using deep learning approaches. Finally, we identify two fundamental issues that require revolutionary solutions in order to move the deep learning technologies from laboratory studies to manufacturing as directions for future efforts. These two issues are analyzed and some preliminary solution directions are proposed.