Data-Driven Optimization of UV-Curable Resin-Based Polymeric Lattice Structures for Predicting Load-Bearing Capacity and Structural Dimensions

Data-Driven Optimization of UV-Curable Resin-Based Polymeric Lattice Structures for Predicting Load-Bearing Capacity and Structural Dimensions | 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 Data-Driven Optimization of UV-Curable Resin-Based Polymeric Lattice Structures for Predicting Load-Bearing Capacity and Structural Dimensions Mainul Hossain, Mohammad Abu Hasan Khondoker This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8888709/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Lattice structures fabricated by additive manufacturing offer significant potential for lightweight, high-performance load-bearing applications; however, identifying optimal lattice geometries for target and maximum mechanical performance remains challenging due to the high cost and time associated with extensive experimental testing. In this study, a novel data-driven optimization framework is proposed to design lattice structures with targeted compressive load-bearing capacity efficiently. Using additive manufacturing, lattice structures with controlled geometric parameters and predefined topologies were fabricated through a layer-by-layer 3D printing process. This approach enables precise control over lattice architecture and repeatable fabrication of complex cellular geometries. A total of 93 lattice specimens representing five different lattice topologies were additively manufactured and experimentally evaluated under compression testing. The resulting dataset, comprising lattice topology, geometric dimensions, and ultimate compressive load, was used to develop surrogate models based on Gradient Boosted Regression. Differential Evolution optimization algorithm to identify optimal lattice dimensions that can achieve specified target and maximum loads within predefined design constraints. The results demonstrate that the proposed hybrid framework effectively captures the nonlinear relationship between lattice geometry and mechanical performance, achieving both target and maximum load-bearing capacity, while enabling accurate and efficient optimization of lattice dimensions and significantly reducing experimental effort. Physical sciences/Engineering Physical sciences/Materials science Additive manufacturing Lattice structure Load bearing capacity Gradient Boosted Regression Differential Evolution Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 18 Mar, 2026 Reviews received at journal 17 Mar, 2026 Reviewers agreed at journal 17 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviewers agreed at journal 07 Mar, 2026 Reviews received at journal 06 Mar, 2026 Reviewers agreed at journal 19 Feb, 2026 Reviewers invited by journal 18 Feb, 2026 Editor invited by journal 18 Feb, 2026 Editor assigned by journal 17 Feb, 2026 Submission checks completed at journal 17 Feb, 2026 First submitted to journal 15 Feb, 2026 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. 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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-8888709","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":594198589,"identity":"f2e624db-0ee8-4e29-9da0-f65a6d68b278","order_by":0,"name":"Mainul Hossain","email":"","orcid":"","institution":"University of Regina","correspondingAuthor":false,"prefix":"","firstName":"Mainul","middleName":"","lastName":"Hossain","suffix":""},{"id":594198590,"identity":"bf3f48ba-98f6-401a-b311-08a742f0a56d","order_by":1,"name":"Mohammad Abu Hasan Khondoker","email":"data:image/png;base64,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","orcid":"","institution":"University of Regina","correspondingAuthor":true,"prefix":"","firstName":"Mohammad","middleName":"Abu Hasan","lastName":"Khondoker","suffix":""}],"badges":[],"createdAt":"2026-02-15 22:53:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8888709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8888709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103505705,"identity":"9bc63c09-d104-4a34-ab9b-5de61c6bae8f","added_by":"auto","created_at":"2026-02-26 13:32:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1569979,"visible":true,"origin":"","legend":"","description":"","filename":"LatticeAndMLPaperFeb2026.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8888709/v1_covered_c79ce352-2da2-4ea1-a719-4c8439590eec.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Data-Driven Optimization of UV-Curable Resin-Based Polymeric Lattice Structures for Predicting Load-Bearing Capacity and Structural Dimensions","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Additive manufacturing, Lattice structure, Load bearing capacity, Gradient Boosted Regression, Differential Evolution","lastPublishedDoi":"10.21203/rs.3.rs-8888709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8888709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eLattice structures fabricated by additive manufacturing offer significant potential for lightweight, high-performance load-bearing applications; however, identifying optimal lattice geometries for target and maximum mechanical performance remains challenging due to the high cost and time associated with extensive experimental testing. 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