  Understanding the thermal behavior of additive manufacturing (AM) processes
is crucial for enhancing the quality control and enabling customized process
design. Most purely physics-based computational models suffer from intensive
computational costs and the need of calibrating unknown parameters, thus not
suitable for online control and iterative design application. Data-driven
models taking advantage of the latest developed computational tools can serve
as a more efficient surrogate, but they are usually trained over a large amount
of simulation data and often fail to effectively use small but high-quality
experimental data. In this work, we developed a hybrid physics-based
data-driven thermal modeling approach of AM processes using physics-informed
neural networks. Specifically, partially observed temperature data measured
from an infrared camera is combined with the physics laws to predict full-field
temperature history and to discover unknown material and process parameters. In
the numerical and experimental examples, the effectiveness of adding auxiliary
training data and using the pretrained model on training efficiency and
prediction accuracy, as well as the ability to identify unknown parameters with
partially observed data, are demonstrated. The results show that the hybrid
thermal model can effectively identify unknown parameters and capture the
full-field temperature accurately, and thus it has the potential to be used in
iterative process design and real-time process control of AM.
