|About this Abstract
||7th World Congress on Integrated Computational Materials Engineering (ICME 2023)
||Microstructure Variability Prediction in Powder Bed Metal Additive Manufacturing
||Aashique Alam Rezwan, Theron M Rodgers, Daniel Moser
|On-Site Speaker (Planned)
||Aashique Alam Rezwan
Laser powder bed fusion (LPBF) for stainless steels can produce components with novel designs and material properties. However, LBPF processes introduce variance in mechanical properties and as built geometries even with identical settings on a single machine. Quantifying the uncertainties introduced by LBPF is essential for component qualification. This work presents probabilistic predictions of microstructure variability in LPBF. A high-fidelity thermal fluid model is used to predict the melt pool behavior (i.e., temperature/phase) and it’s variance due to processing uncertainties. These data are used to predict microstructure size, shape and crystallographic texture using a coupled grain nucleation and kinetic Monte Carlo model for grain growth. Variability due to microstructure model parameters are also considered. This analysis will provide a tool for designers to predict a generalized margin of design for the mechanical properties of additively manufactured parts.
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