Designing alloy from 100% scrap with multi-modal Artificial Intelligence

Designing alloy from 100% scrap with multi-modal Artificial Intelligence | 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 Article Designing alloy from 100% scrap with multi-modal Artificial Intelligence Ye Wei, Chao Yang, Bo Peng, qingzhou Tao, Binhan Sun, Shu Da, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7066209/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Producing alloys from recycled metals can largely reduce the energy consumption, greenhouse gas emissions and environmental damage caused by traditional synthesis starting from ore mining. However, the accumulation of impurities when using a high proportion of recycled metal often leads to the formation of unwanted harmful intermetallic phases and thus deteriorate the performance. Conventional synthesis requires resource-intensive virgin metals to control the overall impurity levels. However, this empirical-driven approach with a low tolerance for impurities often fails to tailor the microstructure and produces alloys with reduced mechanical properties and high cost. Here, we propose a pipeline of Designing Alloys using 100% Scraps by multi-modal Artificial Intelligence. Such design tolerates high impurity levels while enhancing mechanical performance using 100% recycled metals. The design principles are explored by learning from a multi-modal dataset consisting of the composition, processing, and microstructure of ductile irons, a showcase material with a high recycling rate. Specifically, we integrate neural networks, tree optimization to optimize the mechanical properties of ductile irons containing multiple types of impurity elements in high content and introduces a new method to handle the extremely imbalanced data distribution induced by human bias. Compared to the conventional design, our pipeline promotes the recyclability to 100%, reduces the manufacturing cost by 8.1%, improves the tensile strength up to 73.2% and total elongation up to 15.2%, and addresses the strength-ductility trade-off. The systematic experimental characterizations show that the superior mechanical performance derives from synergistic strengthening and damage-tolerant of the well-tailored complex microstructure across multiple length scales. Such counterintuitive strategy leveraging high impurity content as additional design freedom, yet demands precise tuning of chemistry and process parameters beyond human expertise but well-suited to machine learning. This sustainable alloy design strategy provides a universal research approach toward high-value utilization of recycled metallic materials. Physical sciences/Materials science/Structural materials Physical sciences/Mathematics and computing Sustainablity Alloy Design Mutli-modal Artificial Intelligence Full Text Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryInformation.docx Supplementary materials Cite Share Download PDF Status: Posted 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. 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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-7066209","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493593739,"identity":"e0be0bc1-ae19-4423-84e8-557d49e5e8c6","order_by":0,"name":"Ye 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However, the accumulation of impurities when using a high proportion of recycled metal often leads to the formation of unwanted harmful intermetallic phases and thus deteriorate the performance. Conventional synthesis requires resource-intensive virgin metals to control the overall impurity levels. However, this empirical-driven approach with a low tolerance for impurities often fails to tailor the microstructure and produces alloys with reduced mechanical properties and high cost. \r\n\r\n\r\nHere, we propose a pipeline of Designing Alloys using 100% Scraps by multi-modal Artificial Intelligence. Such design tolerates high impurity levels while enhancing mechanical performance using 100\\% recycled metals. The design principles are explored by learning from a multi-modal dataset consisting of the composition, processing, and microstructure of ductile irons, a showcase material with a high recycling rate. Specifically, we integrate neural networks, tree optimization to optimize the mechanical properties of ductile irons containing multiple types of impurity elements in high content and introduces a new method to handle the extremely imbalanced data distribution induced by human bias. Compared to the conventional design, our pipeline promotes the recyclability to 100%, reduces the manufacturing cost by 8.1%, improves the tensile strength up to 73.2% and total elongation up to 15.2%, and addresses the strength-ductility trade-off. The systematic experimental characterizations show that the superior mechanical performance derives from synergistic strengthening and damage-tolerant of the well-tailored complex microstructure across multiple length scales. Such counterintuitive strategy leveraging high impurity content as additional design freedom, yet demands precise tuning of chemistry and process parameters beyond human expertise but well-suited to machine learning. 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