Melt pool dynamics in metal additive manufacturing (AM) is critical to process stability, microstructure formation, and final properties of the printed materials. Physics-based simulation including computational fluid dynamics (CFD) is the dominant approach to predict melt pool dynamics. However, the physics-based simulation approaches suffer from the inherent issue of very high computational cost. This paper provides a physics-informed machine learning (PIML) method by integrating neural networks with the governing physical laws to predict the melt pool dynamics such as temperature, velocity, and pressure without using any training data on velocity. This approach avoids solving the highly non-linear Navier-Stokes equation numerically, which significantly reduces the computational cost. The difficult-to-determine model constants of the governing equations of the melt pool can also be inferred through data-driven discovery. In addition, the physics-informed neural network (PINN) architecture has been optimized for efficient model training. The data-efficient PINN model is attributed to the soft penalty by incorporating governing partial differential equations (PDEs), initial conditions, and boundary conditions in the PINN model.

翻译：金属增材制造（AM）中的熔池动力学对工艺稳定性、微观结构形成以及打印材料的最终性能至关重要。基于物理的模拟方法，包括计算流体动力学（CFD），是预测熔池动力学的主要途径。然而，这类方法存在计算成本极高的固有问题。本文提出一种物理信息机器学习（PIML）方法，通过将神经网络与物理控制定律相结合，在不使用任何速度训练数据的情况下预测熔池动力学（如温度、速度和压力）。该方法避免了数值求解高度非线性的纳维-斯托克斯方程，从而显著降低了计算成本。此外，熔池控制方程中难以确定的模型常数也可通过数据驱动发现进行推断。同时，本文对物理信息神经网络（PINN）架构进行了优化，以实现高效模型训练。这种数据高效的PINN模型归因于在模型中引入了控制偏微分方程（PDE）、初始条件和边界条件的软惩罚项。