Yubin Zhangمشاهده پروفایل
پژوهشگر
Yubin Zhang serves as a Senior Researcher at the Department of Civil and Mechanical Engineering, Technical University of Denmark (DTU), with expertise in advanced materials characterization. His primary affiliation is with DTU Mechanical Engineering (MEK), where he contributes to the 3D Imaging Center (3DIM) project and conducts research on microstructure engineering of metallic materials. His research focuses on Microstructure Engineering, Materials Science, and Tomography, with specialized expertise in aluminum alloys, recrystallization phenomena, nucleation mechanisms, and grain boundary dynamics. He integrates experimental techniques like X-ray microscopy with computational approaches including deep learning for microstructural analysis, enabling precise characterization of deformation and phase transformations in complex materials. Recent publications demonstrate a clear trend toward multimodal 3D/4D imaging techniques combined with machine learning for microstructure quantification. His work bridges fundamental materials science with industrial applications, particularly in additive manufacturing and aluminum processing, where advanced imaging provides critical insights into material behavior during thermo-mechanical treatments. As a dedicated supervisor, Dr. Zhang mentors PhD candidates including Defer, M. C. on additive manufactured AlSiMg alloys, Knipschildt-Okkels, E. F. F. on recrystallization nucleation, and Lindkvist, A. A. on residual stresses in multiphase steels. His research is supported through major grants including the 3DIM project (2016-2029) and Microstructural Engineering of Additive Manufactured AlSiMg (2023-2026), where he serves as Principal Investigator and supervisor respectively. Dr. Zhang operates within DTU's 3D Imaging Center, a state-of-the-art facility utilizing synchrotron radiation and laboratory X-ray sources for non-destructive 3D characterization. His team specializes in diffraction contrast tomography, dark field X-ray microscopy, and machine learning applications for microstructure analysis, with strong collaborations across European research institutions.
