AME Seminar: Kirubel Teferra
Thursday, October 27th, 2022 - 4:00 p.m.
Kirubel Teferra
Multifunctional Materials Branch
U.S. Naval Research Laboratory
"Computational Modeling of Solidification, Residual Stress, and Uncertainty Quantification in Metal Additive Manufacturing"
AME Lecture Hall, Room S202 | Zoom link
Abstract: Additive manufacturing (AM) promises to create lighter, more complex designs that are otherwise too difficult or expensive to build using traditional methods. In contrast to traditional methods, AM part fabrication consists of highly localized and rapid thermally-driven phase changes, giving rise to unique challenges in optimizing material performance. Specifically, the microstructural features of AM-built parts have an extremely complex morphology, defect structure, and residual stress distribution, which vastly differ from wrought-processed counterparts. Further, the build parameters comprise a large design space over which an AM part can be built, leading to extreme variability in the quality of the built part in terms of material integrity and engineering performance. This presentation describes computational models developed to predict microstructural features of AM materials given build parameters in order to alleviate the burden of trial-by-error experimental testing. In particular, an implementation of the cellular automata finite element (CAFE) model has been developed that is capable of simulating large 3D solidified microstructures such that texture analysis and subsequent mechanical analysis over representative volume elements can be performed. Secondly, this model has been coupled with the Multiphysics Object Oriented Simulation Environment (MOOSE) in order to compute microstructure-resolved residual stress as a result of build parameters. Lastly, it is recognized that these models are computationally expensive and preclude the ability for uncertainty quantification. The presentation concludes with a new formulation that combines the stochastic collocation method with physics-informed neural networks with the aim of overcoming the curse of dimensionality associated with propagating parametric uncertainty through computational models. Following the formulation of the models, numerical examples are presented that demonstrate their performance in comparison to experimental data.
Bio: Dr. Kirubel Teferra is a mechanical engineer at the U.S. Naval Research Laboratory (NRL) in the Materials Science & Technology Division since 2015. In 2019 he became a section head within the Multifunctional Materials Branch, where he manages a group of 5 researchers. He works on a breadth of applications pertaining to Computational Mechanics with a focus on theory and algorithm development to improve the predictability and reliability of simulations. Areas he has worked on include micromechanics, biomechanics, and probabilistic methods. He received his Ph.D. from the Department of Civil Engineering and Engineering Mechanics at Columbia University in 2011. Before joining NRL, he held positions as a research engineer at Weidlinger Associates and a postdoctoral fellow in the Department of Civil Engineering at Johns Hopkins University.