Physics-driven Digital Twin for Laser Powder Bed Fusion on GPUs

AutorFerreira, Stephanie; Klein, Benjamin; Stork, Andre; Fellner, Dieter
ArtConference Paper
AbstraktMetal Additive Manufacturing (AM) processes such as Laser Powder Bed Fusion (LPBF) suffer from part distortion due to the localized melting and resolidification of the metal powder, which introduces stresses and strains. Despite becoming more and more important as a manufacturing process, options for simulating the printing process to predict the distortions are limited, especially because existing solutions often require very long computation times. In this work, we present the results of an implementation of the inherent strain method on graphics processing units (GPUs) that exploits the massive parallelism of the many GPU cores to speed up the simulations considerably compared to CPU-based implementations.
KonferenzEuropean Congress on Computational Methods in Applied Sciences and Engineering 2022
ProjektDigital twins bringing agility and innovation to manufacturing SMEs, by empowering a network of DIHs with an integrated digital platform that enables Manufacturing as a Service (MaaS)