Process-induced microstructure modeling in additive manufacturing and friction stir processing
CME Department Seminar
September 8, 2023
11:00 AM - 12:00 PM America/Chicago
842 W. Taylor St., Chicago, IL 60607
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Presenter: Dr. Zhengtao Gan The University of Texas at El Paso
Location: 1047 ERF
n metal additive manufacturing, there is significant flexibility to customize local features, both in terms of geometry and composition. This not only bolsters manufacturing adaptability but also minimizes material wastage. However, the complexity of metal additive manufacturing arises from its multiscale, multiphysics nature, governed by a plethora of parameters. To deeply comprehend the underlying physical mechanisms and the evolution of microstructures in additive manufacturing, we've established a range of multiphysics models, all of which are rigorously validated through experiments. We've identified several universally applicable scaling laws, greatly aiding both process and materials design. Furthermore, we've pioneered physics-embedded graph networks to expedite phase field simulations of grain evolution in laser powder bed fusion, as well as extreme grain deformation and recrystallization in friction stir processing. Utilizing a phase-field approach on the JAX GPU computing platform, we efficiently model grain evolution across both methods. Our study delves into the contrasting grain morphologies and the distinct physical mechanisms characterizing each of these processes.
Dr. Zhengtao Gan earned his Ph.D. in Mechanics from the Chinese Academy of Sciences in 2017. Following his graduation, he began his postdoctoral studies at Northwestern University and was subsequently promoted to a Research Associate in 2019. In 2020, he was appointed Research Assistant Professor. By 2022, Dr. Gan had taken on a new role as an Assistant Professor at the University of Texas at El Paso. His academic and research prowess lies in additive and advanced manufacturing, multiscale and multiphysics modeling, and the integration of scientific machine learning. His exemplary contributions have been recognized with awards, including the Top Performer honor from the Air Force Research Laboratory (AFRL) in 2020 and first-place awards from the National Institute of Standards and Technology (NIST) AM-Bench in both 2018 and 2022.
Nov 1, 2023
Nov 1, 2023