Design optimization of variable thickness stents using machine learning and genetic algorithm

Design optimization of variable thickness stents using machine learning and genetic algorithm | 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 Design optimization of variable thickness stents using machine learning and genetic algorithm Mohamad Khatami, Amir Musa Abazari, Ali Doniavi, Mohammad Fotouhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8162252/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 Utilizing a stent with varying thickness may lead to enhanced performance when compared with a stent of constant thickness. In this study, three new designs of variable thickness stents including external, internal and bilateral variable thickness were investigated using finite element modelling. Taguchi method and analysis of variance were employed to design experiments and to determine the influence of different design variables on the stent equivalent plastic strain value. Subsequently, the optimal variable thickness stent was selected using Random Forest machine learning model and genetic algorithm methods, and it was compared with a constant thickness stent. The designed stents were fabricated with Liquid Crystal Display 3D printing and Polylactic acid resin. The results indicated that the external variable thickness stent with a strut thickness of 0.161 mm and a strut width of 0.176 mm demonstrates the lowest equivalent plastic strain value. The Von-Mises stress distribution in the optimized external variable thickness stent is more uniformly distributed. Compared to the constant thickness stent, the maximum equivalent plastic strains during crimping and expansion in the optimized external variable thickness stent were reduced by 45.7% and 28.9%, respectively. These features make the optimized external variable thickness stent an optimal choice for cardiovascular applications. Variable thickness stent 3D printing Machine learning Genetic algorithm Finite element method 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-8162252","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":556893414,"identity":"a24a2e17-1425-4d5f-8cc0-da3e51fdca40","order_by":0,"name":"Mohamad Khatami","email":"","orcid":"","institution":"Urmia University","correspondingAuthor":false,"prefix":"","firstName":"Mohamad","middleName":"","lastName":"Khatami","suffix":""},{"id":556893415,"identity":"3b90ae09-1452-48d5-b735-424bea945880","order_by":1,"name":"Amir Musa 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algorithm","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":"Variable thickness stent, 3D printing, Machine learning, Genetic algorithm, Finite element method","lastPublishedDoi":"10.21203/rs.3.rs-8162252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8162252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUtilizing a stent with varying thickness may lead to enhanced performance when compared with a stent of constant thickness. In this study, three new designs of variable thickness stents including external, internal and bilateral variable thickness were investigated using finite element modelling. Taguchi method and analysis of variance were employed to design experiments and to determine the influence of different design variables on the stent equivalent plastic strain value. Subsequently, the optimal variable thickness stent was selected using Random Forest machine learning model and genetic algorithm methods, and it was compared with a constant thickness stent. The designed stents were fabricated with Liquid Crystal Display 3D printing and Polylactic acid resin. The results indicated that the external variable thickness stent with a strut thickness of 0.161 mm and a strut width of 0.176 mm demonstrates the lowest equivalent plastic strain value. The Von-Mises stress distribution in the optimized external variable thickness stent is more uniformly distributed. Compared to the constant thickness stent, the maximum equivalent plastic strains during crimping and expansion in the optimized external variable thickness stent were reduced by 45.7% and 28.9%, respectively. 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