Identification of poroid damage in CFRP axle tubes based on modal parameters

Identification of poroid damage in CFRP axle tubes based on modal parameters | 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 Identification of poroid damage in CFRP axle tubes based on modal parameters Lei Feng, Guoping Ding, Yefa Hu, Wenjie Xu, Weiming Yin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6449911/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 9 You are reading this latest preprint version Abstract In order to mitigate the loss caused by poroid damage during the service of carbon fiber-reinforced polymer (CFRP) axle tubes, this paper proposes a modal parameter-based approach to identify poroid damage. The method focuses on single-hole, double-hole, and triple-hole damage as the objects of study, with fiber Bragg grating sensors for data collecting and strain mode shapes serving as the indicator for damage determination. The damage area of the axle tubes is localized based on the difference in strain mode shapes, and the degree of damage is identified using deep neural networks (DNN). The results indicate that the method of identifying the poroid damage of CFRP axle tubes based on modal parameters is highly accurate, with all damage locations reliably identified, and the maximum relative error in damage degree identification is -12.95%. This study is highly significant for enhancing maintenance efficiency and prolonging the service life of CFRP axle tubes. CFRP axle tube strain mode shape DNN FBG sensors damage identification Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Nov, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 25 Sep, 2025 Reviews received at journal 12 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 08 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviewers invited by journal 30 May, 2025 Editor assigned by journal 21 May, 2025 Submission checks completed at journal 16 Apr, 2025 First submitted to journal 14 Apr, 2025 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-6449911","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464830142,"identity":"e810fc27-bce4-4f0c-b0ef-784725c979a0","order_by":0,"name":"Lei Feng","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Feng","suffix":""},{"id":464830143,"identity":"ee7aacf1-384e-4a57-a605-d2aedb176694","order_by":1,"name":"Guoping Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArklEQVRIiWNgGAWjYJCCzwwMNjz8/A1Ea2BmnM3AkCYjOeMAaVoO2xg0JBCpweBG/sHmgl/neQwYDjB++JhDlJZkxuaZfbd5zJkbmCVnbiNCi9mNZPbHvD23eSwbDrAx8xKphbGZt+ccj8GBBFK08Pw4QIIW+zOPDZtnNiTzSM442EycXyTbEx82F/yxs+fnbz744SMxWsCAsQ1MNhCrHgT+kKJ4FIyCUTAKRhwAAHbjONGflg4AAAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Guoping","middleName":"","lastName":"Ding","suffix":""},{"id":464830144,"identity":"2ef63742-1d12-4e4b-a515-e494a29bffaa","order_by":2,"name":"Yefa Hu","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Yefa","middleName":"","lastName":"Hu","suffix":""},{"id":464830145,"identity":"e2dbafd7-06ac-43e3-9d77-1c74609d05d3","order_by":3,"name":"Wenjie Xu","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Xu","suffix":""},{"id":464830146,"identity":"57a83ec8-694b-4f5d-ba09-36af4b8d51eb","order_by":4,"name":"Weiming Yin","email":"","orcid":"","institution":"Wuhan University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Weiming","middleName":"","lastName":"Yin","suffix":""}],"badges":[],"createdAt":"2025-04-15 02:08:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6449911/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6449911/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01297-4","type":"published","date":"2025-11-15T15:58:49+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":96105859,"identity":"84d5eb41-36ff-4ca1-a049-b02b424e4299","added_by":"auto","created_at":"2025-11-17 16:12:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":778005,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6449911/v1_covered_bbbed782-a34b-46cf-b73f-eddafc74ae9a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of poroid damage in CFRP axle tubes based on modal parameters","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-nondestructive-evaluation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jone","sideBox":"Learn more about [Journal of Nondestructive Evaluation](http://link.springer.com/journal/10921)","snPcode":"10921","submissionUrl":"https://submission.nature.com/new-submission/10921/3","title":"Journal of Nondestructive Evaluation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"CFRP axle tube, strain mode shape, DNN, FBG sensors, damage identification","lastPublishedDoi":"10.21203/rs.3.rs-6449911/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6449911/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"In order to mitigate the loss caused by poroid damage during the service of carbon fiber-reinforced polymer (CFRP) axle tubes, this paper proposes a modal parameter-based approach to identify poroid damage. The method focuses on single-hole, double-hole, and triple-hole damage as the objects of study, with fiber Bragg grating sensors for data collecting and strain mode shapes serving as the indicator for damage determination. The damage area of the axle tubes is localized based on the difference in strain mode shapes, and the degree of damage is identified using deep neural networks (DNN). The results indicate that the method of identifying the poroid damage of CFRP axle tubes based on modal parameters is highly accurate, with all damage locations reliably identified, and the maximum relative error in damage degree identification is -12.95%. 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