Hang Yu
Biography
Hang Z. Yu is Professor of Materials Science and Engineering at Virginia Tech. He received his B.S. in Physics from Peking University in 2007 and his Ph.D. in Materials Science and Engineering from MIT in 2013. Following postdoctoral research at MIT, he joined the Virginia Tech faculty in 2016. Professor Yu's research focuses on materials processing and kinetics under external-forcing-driven, far-from-equilibrium conditions, with particular emphasis on applying these scientific insights to solid-state additive manufacturing. His work integrates materials science, manufacturing science, and artificial intelligence to establish process–microstructure–property relationships and accelerate materials development and qualification. He is internationally recognized as a pioneer of Additive Friction Stir Deposition (AFSD) and a leader in solid-state metal additive manufacturing. He is the author of the first dedicated monograph on AFSD, "Additive Friction Stir Deposition" (Elsevier, 2022), and lead editor of the book "Solid-State Metal Additive Manufacturing: Physics, Processes, Mechanical Properties, and Applications" (Wiley-VCH, 2024). His contributions have been recognized through the DARPA Young Faculty Award, and he has been named among the World's Top 2% Scientists (Stanford/Elsevier).
https://mse.vt.edu/faculty-staff/Faculty/yu.html
Abstract
Shear-Driven Solid-State Metal Additive Manufacturing: Leveraging Non-Equilibrium Processing Science for Superior Alloy Performance
As a shear-driven solid-state additive process, additive friction stir deposition (AFSD) not only produces high-performance components with full density and forging-standard mechanical properties but also provides a platform for interrogating material kinetics under sustained external forcing. This process enables access to microstructures and properties unattainable through conventional processing. Unlike fusion-based processes dominated by thermal gradients and cooling rates, AFSD operates in a regime where deformation and thermal histories are intrinsically coupled, and kinetic pathways are governed by defect-mediated transport. Here, we introduce a unified framework integrating process modeling, in situ monitoring, and AI-driven inference to extract voxel-level thermal and deformation histories. These histories delineate distinct processing stages that can be interpreted through non-equilibrium thermodynamics, highlighting the competition between external forcing and thermally activated processes. Building on this understanding, we examine the kinetic phenomena governing dynamic phase and microstructure evolution across multiple length scales, which in turn control key microstructural features such as grain boundary characters and second-phase particle attributes. This framework is demonstrated through the additive repair of aerospace aluminum alloys, where shear-driven fracture and dispersion of constituent particles suppress particle-controlled crack initiation, enabling post-repair fatigue performance that exceeds the pristine condition. These results establish a pathway toward performance-driven alloy design in solid-state additive manufacturing.