|About this Abstract
|Materials Science & Technology 2020
|Additive Manufacturing Modeling and Simulation: AM Materials, Processes, and Mechanics
|Reduced-order Process-structure Linkages during Post-Process Annealing of an Additively Manufactured Ni-base Alloy
|Andrew Marshall, Surya Kalidindi, Bala Radhakrishnan, John Turner
|On-Site Speaker (Planned)
Physics-based multiscale simulations of additive manufacturing are able to quantify the effect of processing conditions (i.e. thermal history) on the evolution of the material structure. However, these simulations are computationally costly, limiting the ability to accurately upscale the model predictions to benefit process optimization. We present physics-aware, reduced-order process-structure linkages for a materials dataset generated from phase field simulations of solid-state transformations during post-process annealing of additively manufactured 625 alloy using a surrogate Ni-Al-Nb ternary alloy. The linkages are developed via Gaussian process regression, a Bayesian machine learning approach that provides prediction uncertainty. Exploiting this property, protocols are developed to provide objective guidance for the selection of new simulations. These protocols allow for rapid identification of the governing physics for the materials phenomena of interest. Research funded by the Department of Energy’s Exascale Computing Project. Phase field simulations were performed using the Oak Ridge Leadership Class Computing Facilties at ORNL.