News: Mapping the future: AI method to transform alloy properties prediction and design Researchers from The Grainger College of Engineering have combined their fundamental knowledge of metals with new machine learning techniques to generate detailed spatial maps. Their method paves the way towards faster and more accurate autonomous material design. In a world of …
Dhruv Anjaria, a Ph.D. candidate from a research group led by Assistant Professor Jean-Charles Stinville of the Department of Materials Science and Engineering at The Grainger College of Engineering, University of Illinois Urbana-Champaign, has received a 2025 Acta Student Award for his key contributions to the paper, “Plastic deformation delocalization at cryogenic temperatures in a nickel-based superalloy.” The …
Ph.D. candidate research wins Acta Student Award Read More »
A groundbreaking study published in 2025 introduces an innovative computer vision-driven framework designed to automatically identify and analyze plastic deformation events from high-resolution digital image correlation (HR‑DIC) data . https://www.sciencedirect.com/science/article/pii/S1044580325006953 Key Highlights: Automated Extraction of Deformation EventsThe method captures localized plastic deformation with high precision, streamlining what has traditionally been a time-consuming and subjective analysis …
New automated framework captures metal plasticity Read More »
Pleased to share our new article published in International Journal of Plasticity:“Strain localization induced by closely spaced lamellae structure in a Mg alloy containing long period stacking ordered structure” https://www.sciencedirect.com/science/article/pii/S0749641925001822 This study advances our fundamental understanding of deformation mechanisms in magnesium alloys and provides guidance for the design of rare-earth-containing Mg alloys. Proud of this …
Modeling the evolution of slip localization: Realization of link to material strength Check out our latest co-authored article, “Modeling the evolution of slip localization: Realization of link to material strength,” recently published in Acta Materialia, in collaboration with the UCSB team. In this work, we combine multiscale modeling with experimental datasets across a wide range …
Assistant Professor Jean-Charles Stinville from the Department of Materials Science and Engineering at The Grainger College of Engineering, University of Illinois Urbana-Champaign, was selected as the inaugural recipient of the Shankari Subramanyam Impact Grant. The honor supports his collaborative research visit to the Indian Institute of Technology Madras in May 2025 Why it matters The SSIG …
New publication details a novel ML-based method for accelerated mechanical properties prediction from encoding and mapping metal plasticity. Encoding metal plasticity captured from high-resolution digital image correlation (DIC) can be leveraged to predict a wide range of monotonic and cyclic macroscopic properties of metallic materials. To capture the spatial heterogeneity of plasticity that develops in …
Plasticity Encoding for Accelerated Mechanical Properties Prediction Read More »
New publication details a novel method for accelerated mechanical properties prediction! We developed a method that integrates additively manufactured functionally graded materials, automated high resolution measurements, and leverage fundamental relationships between local plasticity and macroscopic properties to rapidly predict the fatigue strength of metallic materials. Recent improvements in additive manufacturing and high-throughput material synthesis have …
High-throughput Prediction of Metal Fatigue Strength Read More »
The group published a new study in materials informatics, introducing a machine learning encoding approach for automatically identifying metal microstructures. This advancement paves the way for autonomous metal characterization. Article: https://arxiv.org/abs/2501.18064 Abstract: To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal …
A multi-institution team led by Janelle Wharry has received $2.5 million from the U.S. Department of Energy’s Advanced Research Projects Agency-Energy (ARPA-E) to pursue the design of new materials that could make nuclear fusion power plants a reality. The grant is from ARPA-E’s Creating Hardened And Durable fusion first Wall Incorporating Centralized Knowledge (CHADWICK) program, which aims to explore promising alloy design …
Stinville involved in fusion reactor material research Read More »
