2023/11/14 by Yangfan Li, Satyajit Mojumder, Li, Yangfan +17
Engineering · #Additive Manufacturing Materials and Processes #Additive Manufacturing and 3D Printing Technologies #Computational Engineering #Data Analysis #FOS: Computer and information sciences #FOS: Mathematics #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #Manufacturing Process and Optimization #Numerical Analysis (math.NA) #Statistics and Probability (physics.data-an) #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2311.07821
openalex publication_date 2023/11/14 · openalex created_date 2023/11/16 · openalex updated_date 2026/07/28
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.