About

Jean-Charles Stinville is an Assistant Professor in the Department of Materials Science and Engineering at the University of Illinois Urbana-Champaign. His office is located in the Materials Science and Engineering Building, and he can be contacted via email at jcstinv@illinois.edu or by phone at 217-333-1066. As the Principal Investigator of the Stinville Research Group, he leads research efforts in materials science, contributing to the academic community through his faculty profile, Google Scholar, and vitae. His research focuses on advancing knowledge in materials science and engineering, although specific details of his research interests and key contributions are not provided in the available page text.

Research topics

• Composite material
• Materials science
• Metallurgy
• Political Science
• Mathematics
• Physics
• Geometry

Selected publications

• Statistical Analyses of Plastic Deformation Events via Computer Vision: Case Study of Additive Manufactured Microstructures

  Research Square · 2025-06-10

  preprintOpen accessSenior author
  PublisherOA PDFDOI

• Subsurface microstructure effects on surface resolved slip activity

  Journal of the Mechanics and Physics of Solids · 2025-01-04 · 5 citations

  article
  PublisherDOI

• Learning metal microstructural heterogeneity through spatial mapping of diffraction latent space features

  npj Computational Materials · 2025-09-01 · 6 citations

  articleOpen accessSenior author

  To leverage advancements in machine learning for metallic materials design and property prediction, it is crucial to develop a data-reduced representation of metal microstructures that surpasses the limitations of current physics-based discrete microstructure descriptors. This need is particularly relevant for metallic materials processed through additive manufacturing, which exhibit complex hierarchical microstructures that cannot be adequately described using the conventional metrics typically applied to wrought materials. Furthermore, capturing the spatial heterogeneity of microstructures at the different scales is necessary within such framework to accurately predict their properties. To address these challenges, we propose the physical spatial mapping of metal diffraction latent space features. This approach integrates (i) point diffraction data encoding via variational autoencoders or contrastive learning and (ii) the physical mapping of the encoded values. Together, these steps offer a method to comprehensively describe metal microstructures. We demonstrate this approach on a wrought and additively manufactured alloy, showing that it effectively encodes microstructural information and enables direct identification of microstructural heterogeneity not directly possible by physics-based models. This data-reduced microstructure representation opens the application of machine learning models in accelerating metallic material design and accurately predicting their properties.

  PublisherOA PDFDOI

• Statistical analyses of plastic deformation events via computer vision: Case study of additive manufactured microstructures

  Materials Characterization · 2025-08-05 · 2 citations

  articleSenior authorCorresponding
  PublisherDOI

• Quantitative assessment of the role of local and neighborhood features on the grain-scale response of Inconel 718

  Materials Science and Engineering A · 2025-06-19

  article
  PublisherDOI

• Strain localization induced by closely spaced lamellae structure in a Mg alloy containing long period stacking ordered structure

  International Journal of Plasticity · 2025-07-25 · 10 citations

  articleSenior authorCorresponding
  PublisherDOI

• Accelerated fatigue strength prediction via additive manufactured functionally graded materials and high-throughput plasticity quantification

  Materials & Design · 2025-06-02 · 6 citations

  articleOpen accessSenior authorCorresponding

  Recent improvements in additive manufacturing and high-throughput material synthesis have enabled the discovery of novel metallic materials for extreme environments. However, high-fidelity testing of advanced mechanical properties such as fatigue strength, has often been the most time-consuming and resource-intensive step of material discovery, thereby slowing down the adoption of novel materials. This work presents a new method for rapid characterization of the fatigue properties of many compositions while only testing a single specimen. The approach utilizes high-resolution digital image correlation along with a computer vision model to extract the relationship between localized plastic deformation events and associated mechanical properties. The approach is initially validated on an additive manufactured 316L dataset, then applied to a functionally graded additive manufactured specimen with a composition gradient across the gauge length. This allows for the characterization of multiple compositions, orders of magnitude faster than traditional methods. • High-throughput fatigue strength estimation of many compositions from a single mechanical test. • 20-fold acceleration in fatigue strength prediction. • Functionally graded materials for rapid investigation of fatigue strength.

  PublisherDOI

• Accelerated Fatigue Strength Prediction via Additive Manufactured Functionally Graded Materials and High-Throughput Plasticity Quantification

  arXiv (Cornell University) · 2025-02-16

  preprintOpen accessSenior author

  Recent improvements in additive manufacturing and high-throughput material synthesis have enabled the discovery of novel metallic materials for extreme environments. However, high-fidelity testing of advanced mechanical properties such as fatigue strength, has often been the most time-consuming and resource-intensive step of material discovery, thereby slowing down the adoption of novel materials. This work presents a new method for rapid characterization of the fatigue properties of many compositions while only testing a single specimen. The approach utilizes high-resolution digital image correlation along with a computer vision model to extract the relationship between localized plastic deformation events and associated mechanical properties. The approach is initially validated on an additive manufactured 316L dataset, then applied to a functionally graded additive manufactured specimen with a composition gradient across the gauge length. This allows for the characterization of multiple compositions, orders of magnitude faster than traditional methods.

  PublisherOA PDFDOI

• Quantifying the impact of oxidation on the mechanical properties of Alloy 718 using local mechanical testing techniques

  Materials & Design · 2025-09-09

  articleOpen access

  Despite excellent oxidative properties of the Alloy 718 Ni-based superalloy, long-term exposure to oxidative environments in service creates a chemical gradient in the sub-surface affected by oxidation. Its characterization is key to assessing the evolving mechanical behavior of such affected materials. The present study focuses on the γ'-γ” precipitation depletion induced by the chemical gradient and benchmarks micro-mechanical testing techniques to assess local mechanical properties. Local techniques such as nanoindentation and micro-pillar compression were used to measure both elastic and plastic properties of a pre-oxidized Alloy 718 Ni-based superalloy, having a chemical gradient. These results were compared to a global approach by tensile testing and high resolution-digital image correlation (HR-DIC) on model materials corresponding to regions of the chemical gradient: the solid-solution and the precipitation-hardened Alloy 718. The plastic behavior was investigated in terms of macroscopic yield strength and slip activity. Results obtained by the local and global techniques were found to be different but complementary. The relevance of the association of multiple micro-mechanical tests and sample preparation techniques to probe chemical gradients is discussed and technique advantages and drawbacks are exposed based on the single crystalline or polycrystalline nature of the micro-mechanical testing.

  PublisherDOI

• Dynamic Plastic Deformation Delocalization in FCC Solid Solution Metals

  ArXiv.org · 2025-07-05

  preprintOpen accessSenior author

  Metallic materials undergo irreversible deformation under mechanical loading, leading to intense local plastic localization that reduces their mechanical performance. We identify a new mechanism of plastic deformation localization that dynamically promotes the homogenization of plasticity in face-centered cubic solid solution-strengthened metallic alloys. We observe that this mechanism occurs within a narrow range of stacking fault energies and involves competing deformation between nanoscale twinning and slip. This phenomenon is attributed to a new mechanism referred to as dynamic plastic deformation delocalization, which opens a new design space for enhancing the mechanical performance of metallic materials. We demonstrate that the activation of this mechanism has a significant impact on fatigue properties, greatly enhancing fatigue strength when it occurs.

  PublisherOA PDFDOI

Recent grants

• CAREER: Leveraging Plastic Deformation Mechanisms Interactions in Metallic Materials to Access Extraordinary Fatigue Strength.

  NSF · $374k · 2024–2029

• Collaborative Research: Learning Microstructure- and Temperature-Dependencies of Grain Boundary Plastic Deformation Localization via Multi-modal In situ Characterization

  NSF · $450k · 2023–2027

Frequent coauthors

• Patrick Villechaise

  Centre National de la Recherche Scientifique

  76 shared

• Tresa M. Pollock

  University of California, Santa Barbara

  75 shared

• C. Templier

  Centre Hospitalier Universitaire de Lille

  53 shared

• V. Vallé

  École Nationale Supérieure de Mécanique et d'Aérotechnique

  51 shared

• Damien Texier

  Université de Toulouse

  47 shared

• McLean P. Echlin

  University of California, Santa Barbara

  45 shared

• M. Drouet

  35 shared

• J.P. Rivière

  Université de Poitiers

  35 shared

Labs

• Stinville Research GroupPI

Education

• Ph.D., Materials Science and Engineering

  University of Illinois at Urbana-Champaign

  2000

• M.S., Materials Science and Engineering

  University of Illinois at Urbana-Champaign

  1996

• B.S., Materials Science and Engineering

  University of Illinois at Urbana-Champaign

  1994

Awards & honors

• Teacher Ranked as Excellent (Outstanding) by Their Students…
• Dean’s Award for Excellence in Research (2026)
• Shankari Subramanyam Impact Fellow (2025)
• Acta Student Award (Dhruv Anjaria): Best research paper in A…
• 2024 Stanford/Elsevier World's Top 2% Scientists (2025)