To advance quality assurance in the welding process, this study presents a robust deep learning model that enables the prediction of two critical welds Key Performance Characteristics (KPCs): welding depth and average pore volume. In the proposed approach, a comprehensive range of laser welding Key Input Characteristics (KICs) is utilized, including welding beam geometries, welding feed rates, path repetitions for weld beam geometries, and bright light weld ratios for all paths, all of which were obtained from hairpin welding experiments. Two deep learning networks are employed with multiple hidden dense layers and linear activation functions to showcase the capabilities of deep neural networks in capturing the intricate nonlinear connections inherent within welding KPCs and KICs. Applying deep learning networks to the small numerical experimental hairpin welding dataset has shown promising results, achieving Mean Absolute Error (MAE) values as low as 0.1079 for predicting welding depth and 0.0641 for average pore volume. Additionally, the validity verification demonstrates the reliability of the proposed method. This, in turn, promises significant advantages in controlling welding outcomes, moving beyond the current trend of relying merely on monitoring for defect classification.

翻译：为推进焊接工艺中的质量保障，本研究提出了一种鲁棒的深度学习模型，能够预测两个关键焊接性能特征（KPCs）：熔深与平均孔隙体积。该方法利用了激光焊接关键输入特征（KICs）的全面范围，包括焊接光束几何形状、送丝速度、焊道几何路径重复次数及所有路径的亮光焊缝比率，这些数据均来自发卡焊实验。采用两个包含多层隐藏密集层与线性激活函数的深度学习网络，展示了深度神经网络在捕捉焊接KPCs与KICs间复杂非线性关联方面的能力。将深度学习网络应用于小规模数值实验发卡焊数据集取得了良好结果，熔深预测的平均绝对误差（MAE）低至0.1079，平均孔隙体积预测的MAE为0.0641。此外，有效性验证证明了所提方法的可靠性。这有望在控制焊接结果方面带来显著优势，超越了当前仅依赖监控进行缺陷分类的主流趋势。