Statistical Parameterized Physics-Based Machine Learning Digital Twin
  Models for Laser Powder Bed Fusion Process
Yangfan Li Satyajit Mojumder Ye Lu Abdullah Al Amin Jiachen Guo Xiaoyu Xie Wei Chen Gregory J. Wagner Jian Cao Wing Kam Liu
A digital twin (DT) is a virtual representation of physical process, products
and/or systems that requires a high-fidelity computational model for continuous
update through the integration of sensor data and user input. In the context of
laser powder bed fusion (LPBF) additive manufacturing, a digital twin of the
manufacturing process can offer predictions for the produced parts, diagnostics
for manufacturing defects, as well as control capabilities. This paper
introduces a parameterized physics-based digital twin (PPB-DT) for the
statistical predictions of LPBF metal additive manufacturing process. We
accomplish this by creating a high-fidelity computational model that accurately
represents the melt pool phenomena and subsequently calibrating and validating
it through controlled experiments. In PPB-DT, a mechanistic reduced-order
method-driven stochastic calibration process is introduced, which enables the
statistical predictions of the melt pool geometries and the identification of
defects such as lack-of-fusion porosity and surface roughness, specifically for
diagnostic applications. Leveraging data derived from this physics-based model
and experiments, we have trained a machine learning-based digital twin
(PPB-ML-DT) model for predicting, monitoring, and controlling melt pool
geometries. These proposed digital twin models can be employed for predictions,
control, optimization, and quality assurance within the LPBF process,
ultimately expediting product development and certification in LPBF-based metal
additive manufacturing.