The laser welding process is an important manufacturing technology for metallic materials. However, its application is often hindered by the occurrence of porosity defects. By far, an accurate prediction of the porosity defects and an insight into its formation mechanism are still challenging due to the highly nonlinear physics involved. In this paper, we propose a physics-informed deep learning (PIDL) framework by utilizing mechanistic modeling and experimental data to predict the porosity level during laser beam welding of aluminum alloys. With a proper selection of the physical variables (features) concerning the solidification, liquid metal flow, keyhole stability, and weld pool geometry, the PIDL model shows great superiority in predicting the porosity ratio, with a reduction of mean square error by 41 %, in comparison with the conventional DL model trained with welding parameters. Furthermore, the selected variables are fused into dimensionless features with explicit physical meanings to improve the interpretability and extendibility of the PIDL model. Based on a well-trained PIDL model, the hierarchical importance of the physical variables/procedures on the porosity formation is for the first time revealed with the help of the Shapley Additive Explanations analysis. The keyhole ratio is identified as the most influential factor in the porosity formation, followed by the downward flow-driven drag force, which offers a valuable guideline for process optimization and porosity minimization.

中文翻译：

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预测和洞察激光焊接中孔隙率的形成：一种基于物理学的深度学习框架


激光焊接工艺是金属材料的一项重要制造技术。然而，它的应用经常受到孔隙缺陷的阻碍。到目前为止，由于涉及高度非线性的物理学，准确预测孔隙度缺陷并深入了解其形成机制仍然具有挑战性。在本文中，我们提出了一个物理信息深度学习 （PIDL） 框架，利用机理建模和实验数据来预测铝合金激光束焊接过程中的孔隙率水平。通过适当选择有关凝固、液态金属流动、锁孔稳定性和熔池几何形状的物理变量（特征），PIDL 模型在预测孔隙率方面表现出极大的优势，与使用焊接参数训练的传统 DL 模型相比，均方误差减少了 41 %。此外，将选定的变量融合成具有明确物理含义的无量纲特征，以提高 PIDL 模型的可解释性和可扩展性。基于训练有素的 PIDL 模型，在 Shapley 加法解释分析的帮助下，首次揭示了物理变量/程序对孔隙度形成的分层重要性。锁孔比被确定为孔隙率形成中影响最大的因素，其次是向动驱动的阻力，这为工艺优化和孔隙率最小化提供了有价值的指导。