Directed Energy Deposition Process Modeling, Validation, and Process-Informed Optimization
The directed energy deposition (DED) process, one of the most popular additive manufacturing techniques in use today, involves various complex physical mechanisms that are not yet well understood. In this regard, computational tools show promise for elucidating the manufacturing process and enabling nondestructive performance evaluations of manufactured parts. To better control and optimize the DED […]
Fast and accurate reduced-order modeling of a MOOSE-based additive manufacturing model with operator learning
One predominant challenge in additive manufacturing (AM) is to achieve specific material properties by manipulating manufacturing process parameters during the runtime. Such manipulation tends to increase the computational load imposed on existing simulation tools employed in AM. The goal of the present work is to construct a fast and accurate reduced-order model (ROM) for an […]
Bayesian Inverse Uncertainty Quantification of a MOOSE-based Melt Pool Model for Additive Manufacturing using Experimental Data
Additive Manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas because of its ability to rapidly produce, prototype, and customize designs. AM techniques has significant opportunities in nuclear materials with accelerated fabrication process and reduced cost. High-fidelity modeling and simulation of AM processes is being developed at the Idaho National […]