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

Statistical Analyses of Plastic Deformation Events via Computer Vision: Case Study of Additive Manufactured Microstructures | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Statistical Analyses of Plastic Deformation Events via Computer Vision: Case Study of Additive Manufactured Microstructures Christopher Bean, Mathieu Calvat, Dhruv Anjaria, Edward G. Lukhanin, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6857323/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Materials Characterization → Version 1 posted You are reading this latest preprint version Abstract With the ongoing development of high-throughput characterization approaches for metallic materials, full-field measurements and their rapid statistical analysis have become increasingly essential for capturing material heterogeneities and providing a comprehensive understanding of metal processing and microstructure effects. In the context of mechanical properties, understanding the influence of microstructure on the localization of plasticity at small scales is crucial for describing a material’s behavior. While experimental techniques for measuring plasticity continue to improve, there remains a significant need for rapid and statistically robust analysis methods of resulting data. Here, we propose a framework based on computer vision and event merging for the automated extraction of deformation events from high-resolution digital image correlation measurements. Notably, the proposed approach demonstrates versatility, enabling its application with different material systems, microstructure, and deformation temperature conditions ranging from room to elevated temperatures. A case study is presented to statistically evaluate the impact of processing methods, (i.e., wrought vs additive manufacturing), on the distribution of deformation events during monotonic loading of 718 alloy. Significant differences in plastic localization behavior are observed between the wrought and additively manufactured 718 materials. These differences are discussed in light of their microstructural features. Plastic localization Plastic Deformation Events Computer Vision-based Segmentation High Resolution Digital Image Correlation Additive Manufacturing. Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 05 Aug, 2025 Read the published version in Materials Characterization → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6857323","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":468818912,"identity":"6267b777-6aff-4115-b3c2-d036edc6d9f3","order_by":0,"name":"Christopher Bean","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Christopher","middleName":"","lastName":"Bean","suffix":""},{"id":468818913,"identity":"348a2e40-6591-4b70-ae43-f8ed286a4038","order_by":1,"name":"Mathieu Calvat","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Mathieu","middleName":"","lastName":"Calvat","suffix":""},{"id":468818914,"identity":"87e87962-e170-40c2-8d7b-f68af119ea9f","order_by":2,"name":"Dhruv Anjaria","email":"","orcid":"","institution":"University of Illinois at Urbana-Champaign","correspondingAuthor":false,"prefix":"","firstName":"Dhruv","middleName":"","lastName":"Anjaria","suffix":""},{"id":468818915,"identity":"235d781c-49b3-4b0a-b148-b8540f54ed51","order_by":3,"name":"Edward G. 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