Multiscale sensitivity analysis of elasticity properties for woven composites using data-driven self-consistent clustering analysis and artificial neural network

Multiscale sensitivity analysis of elasticity properties for woven composites using data-driven self-consistent clustering analysis and artificial neural network | 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 Multiscale sensitivity analysis of elasticity properties for woven composites using data-driven self-consistent clustering analysis and artificial neural network Xinxing Han, Xinliang Zhao, Bo Gao, Tao Xu, Kunjie Wang, Chenghai Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7514191/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 Sensitivity analysis of material microscopic properties like matrix and fiber parameters on macroscopic performance is critical for the refined design of the mechanical properties of woven composites. However, this work remains challenging due to the high computational cost of traditional multiscale methods. In this study, a method integrating data-driven self-consistent clustering analysis (SCA) and artificial neural network (ANN) is proposed to address this issue. First, the SCA × SCA multiscale framework is employed to predict macroscale elastic properties through microscale and mesoscale representative volume element based on homogenization. A high-accuracy database linking microscale and macroscale elasticity properties is efficiently generated. Subsequently, an ANN surrogate model is established and trained using this database for rapid prediction of macroscale properties. Last, combined with the improved Fourier amplitude sensitivity test, the cross-scale parameter sensitivity correlation is obtained. The results indicate that the in-plane equivalent modulus of the material is mainly influenced by the axial modulus of the fiber monofilament, while the out of plane equivalent modulus and in-plane shear modulus are mainly influenced by the transverse modulus and Poisson's ratio of the fiber monofilament. Poisson's ratio is mainly influenced by the modulus and Poisson's ratio of the fiber monofilament in the corresponding direction. Sensitivity analysis Self-consistent clustering analysis Artificial neural network Multiscale modeling 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-7514191","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":515430386,"identity":"6f1e6984-6470-4992-94a9-cf7a7a067c66","order_by":0,"name":"Xinxing Han","email":"","orcid":"","institution":"China Aerodynamics Research and Development Center","correspondingAuthor":false,"prefix":"","firstName":"Xinxing","middleName":"","lastName":"Han","suffix":""},{"id":515430387,"identity":"329cba4e-9334-44df-b18a-2f3d052c7abf","order_by":1,"name":"Xinliang Zhao","email":"","orcid":"","institution":"Harbin Institute of 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network","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":"Sensitivity analysis, Self-consistent clustering analysis, Artificial neural network, Multiscale modeling","lastPublishedDoi":"10.21203/rs.3.rs-7514191/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7514191/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSensitivity analysis of material microscopic properties like matrix and fiber parameters on macroscopic performance is critical for the refined design of the mechanical properties of woven composites. However, this work remains challenging due to the high computational cost of traditional multiscale methods. In this study, a method integrating data-driven self-consistent clustering analysis (SCA) and artificial neural network (ANN) is proposed to address this issue. First, the SCA \u0026times; SCA multiscale framework is employed to predict macroscale elastic properties through microscale and mesoscale representative volume element based on homogenization. A high-accuracy database linking microscale and macroscale elasticity properties is efficiently generated. Subsequently, an ANN surrogate model is established and trained using this database for rapid prediction of macroscale properties. Last, combined with the improved Fourier amplitude sensitivity test, the cross-scale parameter sensitivity correlation is obtained. The results indicate that the in-plane equivalent modulus of the material is mainly influenced by the axial modulus of the fiber monofilament, while the out of plane equivalent modulus and in-plane shear modulus are mainly influenced by the transverse modulus and Poisson's ratio of the fiber monofilament. Poisson's ratio is mainly influenced by the modulus and Poisson's ratio of the fiber monofilament in the corresponding direction.\u003c/p\u003e","manuscriptTitle":"Multiscale sensitivity analysis of elasticity properties for woven composites using data-driven self-consistent clustering analysis and artificial neural network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-23 18:53:04","doi":"10.21203/rs.3.rs-7514191/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"4320a13c-5e0e-4f1c-ab4c-a5d718c65ef0","owner":[],"postedDate":"September 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T16:41:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-23 18:53:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7514191","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7514191","identity":"rs-7514191","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}