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.

结合机器学习和物理原理，利用少量标记数据，成功预测了 3D 金属添加制造中的温度和熔池动态。