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A digital twin (DT) is a virtual representation of physical process, productsand/or systems that requires a high-fidelity computational model for continuousupdate through the integration of sensor data and user input. In the context oflaser powder bed fusion (LPBF) additive manufacturing, a digital twin of themanufacturing process can offer predictions for the produced parts, diagnosticsfor manufacturing defects, as well as control capabilities. This paperintroduces a parameterized physics-based digital twin (PPB-DT) for thestatistical predictions of LPBF metal additive manufacturing process. Weaccomplish this by creating a high-fidelity computational model that accuratelyrepresents the melt pool phenomena and subsequently calibrating and validatingit through controlled experiments. In PPB-DT, a mechanistic reduced-ordermethod-driven stochastic calibration process is introduced, which enables thestatistical predictions of the melt pool geometries and the identification ofdefects such as lack-of-fusion porosity and surface roughness, specifically fordiagnostic applications. Leveraging data derived from this physics-based modeland experiments, we have trained a machine learning-based digital twin(PPB-ML-DT) model for predicting, monitoring, and controlling melt poolgeometries. 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 metaladditive manufacturing.

Statistical Parameterized Physics-Based Machine Learning Digital Twin Models for Laser Powder Bed Fusion Process