The recent explosion of machine learning (ML) and artificial intelligence (AI) shows great potential in the breakthrough of metal additive manufacturing (AM) process modeling. However, the success of conventional machine learning tools in data science is primarily attributed to the unprecedented large amount of labeled data-sets (big data), which can be either obtained by experiments or first-principle simulations. Unfortunately, these labeled data-sets are expensive to obtain in AM due to the high expense of the AM experiments and prohibitive computational cost of high-fidelity simulations. We propose a physics-informed neural network (PINN) framework that fuses both data and first physical principles, including conservation laws of momentum, mass, and energy, into the neural network to inform the learning processes. To the best knowledge of the authors, this is the first application of PINN to three dimensional AM processes modeling. Besides, we propose a hard-type approach for Dirichlet boundary conditions (BCs) based on a Heaviside function, which can not only enforce the BCs but also accelerate the learning process. The PINN framework is applied to two representative metal manufacturing problems, including the 2018 NIST AM-Benchmark test series. We carefully assess the performance of the PINN model by comparing the predictions with available experimental data and high-fidelity simulation results. The investigations show that the PINN, owed to the additional physical knowledge, can accurately predict the temperature and melt pool dynamics during metal AM processes with only a moderate amount of labeled data-sets. The foray of PINN to metal AM shows the great potential of physics-informed deep learning for broader applications to advanced manufacturing.

中文翻译：

---

金属增材制造的机器学习：使用物理神经网络预测温度和熔池流体动力学

最近机器学习 (ML) 和人工智能 (AI) 的爆炸式增长显示了金属增材制造 (AM) 工艺建模突破的巨大潜力。然而，传统机器学习工具在数据科学中的成功主要归功于前所未有的大量标记数据集（大数据），这些数据集可以通过实验或第一性原理模拟获得。不幸的是，由于 AM 实验的高昂费用和高保真模拟的高昂计算成本，在 AM 中获得这些标记数据集的成本很高。我们提出了一个物理信息神经网络 (PINN) 框架，该框架将数据和第一物理原理（包括动量、质量和能量守恒定律）融合到神经网络中，以告知学习过程。据作者所知，这是 PINN 首次应用于三维 AM 过程建模。此外，我们提出了一种基于 Heaviside 函数的狄利克雷边界条件 (BC) 的硬型方法，该方法不仅可以强制执行 BC，还可以加速学习过程。PINN 框架应用于两个具有代表性的金属制造问题，包括 2018 NIST AM-Benchmark 测试系列。我们通过将预测与可用的实验数据和高保真模拟结果进行比较来仔细评估 PINN 模型的性能。研究表明，PINN 由于额外的物理知识，可以准确地预测金属 AM 工艺过程中的温度和熔池动态，仅需要适量的标记数据集。