Enhancing Computed Tomography-based pore mesh models through matching with microscope cross-section images

Enhancing Computed Tomography-based pore mesh models through matching with microscope cross-section images | 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 Enhancing Computed Tomography-based pore mesh models through matching with microscope cross-section images Sebastian Mansky, Malte Becker, Dirk Herzog, Ingomar Kelbassa This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6664141/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted 12 You are reading this latest preprint version Abstract X-Ray Computed Tomography (CT) is a widely adopted tool in the non-destructive quality assurance of additive manufacturing (AM). Porosity in AM can be assessed via CT without compromising the integrity of the part and without reliance on witness specimen. Reliable pore criticality analysis, essential for AM fatigue assessments, hinges on precise determination of pore dimensions. This work investigates CT data by comparing the pore sizes and shapes from two different data sources (CT and metallography), originating from the same samples. The comparison indicates a pore size underestimation in the CT data. A subsequent rescaling and smoothing workflow on the CT pore data compensates this underestimation. This workflow reduces the mean pore size deviations between both data sources, allowing a more accurate pore assessment. Additionally the smoothing process reduces errors introduced by the CT reconstruction. The rescaled and smoothed pores serve as an improved starting point for investigations regarding the effect of porosity on fatigue in AM. Additive manufacturing X-ray Computed Tomography Smoothing Microscope images Cross-sections Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Aug, 2025 Read the published version in Journal of Nondestructive Evaluation → Version 1 posted Editorial decision: Revision requested 06 Jun, 2025 Reviews received at journal 04 Jun, 2025 Reviews received at journal 31 May, 2025 Reviewers agreed at journal 24 May, 2025 Reviewers agreed at journal 22 May, 2025 Reviewers agreed at journal 21 May, 2025 Reviews received at journal 21 May, 2025 Reviewers agreed at journal 19 May, 2025 Reviewers invited by journal 19 May, 2025 Editor assigned by journal 19 May, 2025 Submission checks completed at journal 15 May, 2025 First submitted to journal 14 May, 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. 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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-6664141","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":459195636,"identity":"f6e14203-3414-44e0-91f9-6489c04a8653","order_by":0,"name":"Sebastian Mansky","email":"data:image/png;base64,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","orcid":"","institution":"Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT","correspondingAuthor":true,"prefix":"","firstName":"Sebastian","middleName":"","lastName":"Mansky","suffix":""},{"id":459195637,"identity":"54e6b042-f926-4003-88a4-50495331682b","order_by":1,"name":"Malte Becker","email":"","orcid":"","institution":"Fraunhofer Research Institution for Additive Manufacturing Technologies IAPT","correspondingAuthor":false,"prefix":"","firstName":"Malte","middleName":"","lastName":"Becker","suffix":""},{"id":459195639,"identity":"e0bf71c0-099a-4729-85d8-25b89cce69b1","order_by":2,"name":"Dirk Herzog","email":"","orcid":"","institution":"Institute for Industrialization of Smart Materials","correspondingAuthor":false,"prefix":"","firstName":"Dirk","middleName":"","lastName":"Herzog","suffix":""},{"id":459195640,"identity":"bd474ed5-ca53-464e-8f84-52e81cf68e8e","order_by":3,"name":"Ingomar Kelbassa","email":"","orcid":"","institution":"Institute for Industrialization of Smart Materials","correspondingAuthor":false,"prefix":"","firstName":"Ingomar","middleName":"","lastName":"Kelbassa","suffix":""}],"badges":[],"createdAt":"2025-05-14 12:23:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6664141/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6664141/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10921-025-01240-7","type":"published","date":"2025-08-18T16:29:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89847810,"identity":"ea3aab58-4667-4297-9685-2be4cca036d2","added_by":"auto","created_at":"2025-08-25 16:44:19","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3048592,"visible":true,"origin":"","legend":"","description":"","filename":"paper.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6664141/v1_covered_a150489e-3231-4f13-8113-f840c9740a15.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhancing Computed Tomography-based pore mesh models through matching with microscope cross-section images","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":"Additive manufacturing, X-ray Computed Tomography, Smoothing, Microscope images, Cross-sections","lastPublishedDoi":"10.21203/rs.3.rs-6664141/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6664141/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eX-Ray Computed Tomography (CT) is a widely adopted tool in the non-destructive quality assurance of additive manufacturing (AM). 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