Bridging the Simulation Gap: Physics-Interpretable Machine Learning Framework for In Silico Fatigue Life Prediction of Additively Manufactured Polymers

Bridging the Simulation Gap: Physics-Interpretable Machine Learning Framework for In Silico Fatigue Life Prediction of Additively Manufactured Polymers | 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 Bridging the Simulation Gap: Physics-Interpretable Machine Learning Framework for In Silico Fatigue Life Prediction of Additively Manufactured Polymers Rafael Meyer This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8735376/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 rapid adoption of additively manufactured components in structural applications exposes a fundamental limitation of current engineering workflows: process parameter selection and performance prediction are often treated as disconnected tasks. To address this gap, this study proposes an integrated and data-efficient framework that combines statistical design of experiments, ensemble machine learning, and interpretable artificial intelligence to predict the fatigue life of fused deposition modeling (FDM) printed polylactic acid (PLA). A Plackett–Burman design was employed to efficiently explore a seven-dimensional parameter space using only 64 experimentally tested specimens. Based on this dataset, a Random Forest ensemble model achieved a coefficient of determination of R^2 = 0.996 on an independent test set. Model interpretability is ensured through SHapley Additive exPlanations (SHAP), which identify wall thickness and infill density as the dominant fatigue-governing parameters, while revealing nonlinear effects associated with nozzle temperature. Predictive uncertainty is quantified via Monte Carlo simulations, enabling the extraction of reliability-oriented metrics such as P90 and P95. With microsecond-scale inference times, the proposed framework enables real-time process optimization and supports the development of digital twins in additive manufacturing. Additive manufacturing FDM fatigue prediction Random Forest ensemble machine learning interpretability (SHAP) Small Data 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. 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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-8735376","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":585008058,"identity":"7ac6df85-4a8d-4580-b4ea-e8a45c775622","order_by":0,"name":"Rafael Meyer","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal de Juiz de Fora","correspondingAuthor":true,"prefix":"","firstName":"Rafael","middleName":"","lastName":"Meyer","suffix":""}],"badges":[],"createdAt":"2026-01-29 22:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8735376/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8735376/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103050623,"identity":"2e0f7dfc-0326-41b9-b627-a17ac6d15709","added_by":"auto","created_at":"2026-02-20 07:50:57","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":487477,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptRafaelMeyer1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8735376/v1_covered_6c55d568-1285-4649-bf58-001d321d7d48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Bridging the Simulation Gap: Physics-Interpretable Machine Learning Framework for In Silico Fatigue Life Prediction of Additively Manufactured Polymers","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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, FDM, fatigue prediction, Random Forest ensemble, machine learning interpretability (SHAP), Small Data","lastPublishedDoi":"10.21203/rs.3.rs-8735376/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8735376/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The rapid adoption of additively manufactured components in structural applications exposes a fundamental limitation of current engineering workflows: process parameter selection and performance prediction are often treated as disconnected tasks. 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