Predicting Pitting Potential of Additively Manufactured Stainless Steel using Machine Learning

Predicting Pitting Potential of Additively Manufactured Stainless Steel using Machine Learning | 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 Predicting Pitting Potential of Additively Manufactured Stainless Steel using Machine Learning David Montes Oca Zapiain, Michael Melia, Ryan Katona This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9213876/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 The heterogeneous corrosion response of metal additive manufacturing (AM) parts caused by the variability in the printed parts hinders their broad adoption and implementation. Existing corrosion response characterization protocols rely on experimental observations that, while useful, are limited to providing qualitative guidance on the performance of new printed parts. In this work, a protocol for establishing a robust and predictive model that links the corrosion behavior to its corresponding processing parameters and as-printed part descriptors is developed. The developed protocol distills a set of features from the AM inputs with unsuper-vised learning and subsequently uses ensembling models to build a robust predictive model for the corrosion behavior of AM parts. This protocol is validated by predicting the electrochemical breakdown potential of as printed, AM stainless steel 316L as a function of AM printers and heat treatments. The developed framework showcases a practical pathway to leverage prior experimental data to rapidly estimate corrosion response in AM stainless steel. Additive Manufacturing Machine Learning Principal Component Full Text Additional Declarations No competing interests reported. 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. 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-9213876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":614149274,"identity":"fbcf066f-0146-459c-bc79-d0d4c55b7a47","order_by":0,"name":"David Montes Oca Zapiain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYDCCA2DSBkQYHCBeywGGNNK1HAZrIU4H37XDxx5/qDhvzz+7eeOBHwy1cgTtkrydlm5w4MztxBl3jhUc7GE4bkxQi8HtHDOJg223Exhu5Bgc4GE4ljizgSgt/87ZywO1HPxDvJaGA4wbgFoO8zDUJPYT0AHyS5rEmWPJiRtvpBUcljE4YMxPSAvf7eRjEhU1dvZyN5I3f3xTUSfHRkgLujsPk6gBCOpI1zIKRsEoGAXDHgAAzPtKci8EdWsAAAAASUVORK5CYII=","orcid":"","institution":"Sandia National Laboratories","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"Montes Oca","lastName":"Zapiain","suffix":""},{"id":614149275,"identity":"5ee3b52a-912a-4d2b-ba26-184873f127bf","order_by":1,"name":"Michael Melia","email":"","orcid":"","institution":"Sandia National Laboratories","correspondingAuthor":false,"prefix":"","firstName":"Michael","middleName":"","lastName":"Melia","suffix":""},{"id":614149276,"identity":"07903095-c7c2-467c-b3dc-5ddef1536a18","order_by":2,"name":"Ryan Katona","email":"","orcid":"","institution":"Sandia National Laboratories","correspondingAuthor":false,"prefix":"","firstName":"Ryan","middleName":"","lastName":"Katona","suffix":""}],"badges":[],"createdAt":"2026-03-24 15:24:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9213876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9213876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105904339,"identity":"16e0ec0c-74a1-416e-8e53-b6f12029a01b","added_by":"auto","created_at":"2026-04-01 10:07:37","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":492795,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9213876/v1_covered_c8474626-22c1-4e24-b74a-8177ca24e99e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Predicting Pitting Potential of Additively Manufactured Stainless Steel using Machine Learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Additive Manufacturing, Machine Learning, Principal Component","lastPublishedDoi":"10.21203/rs.3.rs-9213876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9213876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The heterogeneous corrosion response of metal additive manufacturing (AM) parts caused by the variability in the printed parts hinders their broad adoption and implementation. 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