Topology Optimization and Machine Learning-Based Parametric Optimization Techniques: A Comparative Study With Physical Validation

Topology Optimization and Machine Learning-Based Parametric Optimization Techniques: A Comparative Study With Physical Validation | 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 Topology Optimization and Machine Learning-Based Parametric Optimization Techniques: A Comparative Study With Physical Validation Ankur Kapileshwar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7322757/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 Topology optimization (TO) and Parametric Optimization (PO) are two fundamental structural optimization (SO) techniques. However, the current literature is not clear as to which optimization workflow is better suited for simulating and predicting real-world results, especially in the context of other factors relevant to deciding which workflow to use. The aim of this study is therefore to understand how these two techniques stack up against each other from a variety of perspectives − (1) time taken for optimization, (2) manufacturability, (3) real-life adherence to simulated results, and (4) overall stiffness. Both workflows are evaluated and compared systematically on a cantilever beam and with different volume fractions (VF) of beams − 30%, 50%, and 80%. The results achieved via software simulation for both PO and TO are then validated in a real-world physical setting. The simulated versus physically validated results are compared for all six TO and PO optimized cantilever beams. It was found that TO is faster and shows better stiffness, while PO is more manufacturable and shows better predictability between software-simulated and physically validated results. The conclusions of this study will assist structural designers in their choice of optimization workflow, given their design necessities and constraints. Topology Optimization Parametric Optimization Structural Optimization Machine Learning Cantilever Beam 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-7322757","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":497739337,"identity":"95c54ef1-79f4-4e01-a46f-54080a8b995f","order_by":0,"name":"Ankur Kapileshwar","email":"data:image/png;base64,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","orcid":"","institution":"","correspondingAuthor":true,"prefix":"","firstName":"Ankur","middleName":"","lastName":"Kapileshwar","suffix":""}],"badges":[],"createdAt":"2025-08-08 02:38:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7322757/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7322757/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89404903,"identity":"96d72468-c893-45c5-ab5a-430ceee87679","added_by":"auto","created_at":"2025-08-19 15:02:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":723706,"visible":true,"origin":"","legend":"","description":"","filename":"springer.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7322757/v1_covered_84ff3e6b-8f5f-486f-9f18-1812b7a4e403.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Topology Optimization and Machine Learning-Based Parametric Optimization Techniques: A Comparative Study With Physical Validation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Topology Optimization, Parametric Optimization, Structural Optimization, Machine Learning, Cantilever Beam","lastPublishedDoi":"10.21203/rs.3.rs-7322757/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7322757/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTopology optimization (TO) and Parametric Optimization (PO) are two fundamental structural optimization (SO) techniques. However, the current literature is not clear as to which optimization workflow is better suited for simulating and predicting real-world results, especially in the context of other factors relevant to deciding which workflow to use. The aim of this study is therefore to understand how these two techniques stack up against each other from a variety of perspectives \u0026minus;\u0026thinsp;(1) time taken for optimization, (2) manufacturability, (3) real-life adherence to simulated results, and (4) overall stiffness. Both workflows are evaluated and compared systematically on a cantilever beam and with different volume fractions (VF) of beams \u0026minus;\u0026thinsp;30%, 50%, and 80%. The results achieved via software simulation for both PO and TO are then validated in a real-world physical setting. The simulated versus physically validated results are compared for all six TO and PO optimized cantilever beams. 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