Attend a seminar on the microstructure analytics of Ni-based superalloys using computer vision techniques, Jan. 23
About
Sammy Tin is the department head, Patrick R. Taylor Endowed Department Leadership Chair and a professor of materials science and engineering at the University of Arizona. He previously held the Charles and Lee Finkl Chair of Materials Engineering and served as director of the Thermal Processing Technology Center at the Illinois Institute of Technology. Tin is a Fellow of ASM International and earned a bachelor’s degree in materials engineering from California Polytechnic State University, San Luis Obispo, followed by a master’s degree from Carnegie Mellon University and a doctoral degree from the University of Michigan, all in materials science and engineering.
His research focuses on understanding how alloy composition and manufacturing processes influence a material’s internal structure, mechanical behavior and real-world performance across a wide range of structural alloys. He serves on the editorial committees and has received multiple international awards recognizing his contributions to the field. Tin has also held leadership roles in professional societies. He holds three patents, has two patents pending and has authored more than 150 peer-reviewed journal articles and conference papers.
Abstract
Recent advances in materials characterization now allow researchers to collect extremely detailed information about the internal structures of metals and alloys. Instead of measuring only basic features like grain size or particle size, modern imaging techniques can generate rich datasets that reveal how materials vary from place to place at very small scales. By applying computer vision and artificial intelligence tools to commonly used microscopy data, researchers can extract new quantitative measures that help explain how a material’s structure changes with its chemical composition or how it was manufactured. This growing area, often called microstructure analytics or informatics, makes it possible to describe materials in ways that were not previously achievable across large datasets. These methods capture the natural variability that exists inside real engineering materials, rather than relying on simplified averages. In this seminar, case studies will show how microstructure analytics has led to new insights into abnormal grain growth in advanced nickel-based superalloys and how metal structures change during additive manufacturing. The same data can also be used to help predict material behavior, improve computer models and support the development of digital twins that link processing, structure and performance.
Microstructure analytics of Ni-based superalloys using computer vision techniques seminar
Friday, Jan. 23, 2025
11 a.m.–noon
Interdisciplinary Science and Technology Building 12 (ITSB12) room 313, Polytechnic campus [map]