Compared to physics-based computational manufacturing, data-driven models such as machine learning (ML) are alternative approaches to achieve smart manufacturing. However, the data-driven ML's "black box" nature has presented a challenge to interpreting its outcomes. On the other hand, governing physical laws are not effectively utilized to develop data-efficient ML algorithms. To leverage the advantages of ML and physical laws of advanced manufacturing, this paper focuses on the development of a physics-informed machine learning (PIML) model by integrating neural networks and physical laws to improve model accuracy, transparency, and generalization with case studies in laser metal deposition (LMD).

翻译：相较于基于物理的计算制造方法，数据驱动模型（如机器学习）是实现智能制造的替代途径。然而，数据驱动机器学习的"黑箱"特性对其结果的可解释性构成了挑战。另一方面，控制物理定律尚未被有效用于开发数据高效的机器学习算法。为结合机器学习优势与先进制造的物理规律，本文聚焦于开发一种物理信息机器学习模型，通过集成神经网络与物理定律，以激光金属沉积为例，提升模型的准确性、透明度和泛化能力。