Automated non-destructive characterization for the cutting surfaces of punched holes using a laser profiler

Automated non-destructive characterization for the cutting surfaces of punched holes using a laser profiler | 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 Automated non-destructive characterization for the cutting surfaces of punched holes using a laser profiler Daniel Luca Hahn, Jakub Korenek, Matthias Riemer, Verena Kräusel, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8609328/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract The quality of punched hole surfaces significantly influences the formability in subsequent processes such as collar forming, especially in the occurrence of edge cracks. Traditional evaluation based on metallographic cross-sections is unsuitable for inline inspection. This has prompted the development of novel approaches for the inline characterization of the functional cutting surfaces. Such methods also enable the systematic acquisition of production data required for data-driven models that enhance robustness against stochastic variations in multi-stage forming processes. This study proposes a non-destructive, automated method for characterizing punched hole cutting surfaces using a laser line profiler. An experimental setup with adjustable inclination and a rotation stage for automated sample rotation was constructed. Punched samples of S500MC steel and EN AW-1050A aluminum with different sheet thicknesses were investigated. A python algorithm was developed to automatically identify characteristic features of the cutting surface according to VDI 2906-2. Validation using optical microscopy of metallographic cross-sections confirmed the accuracy of the method. A mean absolute percentage error (MAPE) below 5% was achieved for burnish and fracture zone height. The results demonstrate the potential of laser profiling for non-destructive characterization of punched hole cutting surfaces, establishing the method’s feasibility and laying the groundwork for transfer to near-industrial conditions. Initially validated under laboratory settings, the proposed method will be applied in a progressive forming tool to assess the impact of real-world disturbances such as dust and vibration. While the laboratory results are promising, further optimization of the measurement technique and data evaluation is required for full industrial implementation. Non-destructive measurement shear cutting sheared surface quality data-driven modelling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 10 Feb, 2026 Reviews received at journal 10 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers agreed at journal 21 Jan, 2026 Reviewers invited by journal 19 Jan, 2026 Editor assigned by journal 16 Jan, 2026 Submission checks completed at journal 16 Jan, 2026 First submitted to journal 15 Jan, 2026 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-8609328","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":577478163,"identity":"15b38e24-b4ab-459e-a586-a38292d3cd33","order_by":0,"name":"Daniel Luca 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profiler","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":"production-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Production Engineering](https://www.springer.com/journal/11740)","snPcode":"11740","submissionUrl":"https://submission.nature.com/new-submission/11740/3","title":"Production Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Non-destructive measurement, shear cutting, sheared surface quality, data-driven modelling","lastPublishedDoi":"10.21203/rs.3.rs-8609328/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8609328/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe quality of punched hole surfaces significantly influences the formability in subsequent processes such as collar forming, especially in the occurrence of edge cracks. Traditional evaluation based on metallographic cross-sections is unsuitable for inline inspection. This has prompted the development of novel approaches for the inline characterization of the functional cutting surfaces. Such methods also enable the systematic acquisition of production data required for data-driven models that enhance robustness against stochastic variations in multi-stage forming processes. This study proposes a non-destructive, automated method for characterizing punched hole cutting surfaces using a laser line profiler. An experimental setup with adjustable inclination and a rotation stage for automated sample rotation was constructed. Punched samples of S500MC steel and EN AW-1050A aluminum with different sheet thicknesses were investigated. A python algorithm was developed to automatically identify characteristic features of the cutting surface according to VDI 2906-2. Validation using optical microscopy of metallographic cross-sections confirmed the accuracy of the method. A mean absolute percentage error (MAPE) below 5% was achieved for burnish and fracture zone height. The results demonstrate the potential of laser profiling for non-destructive characterization of punched hole cutting surfaces, establishing the method\u0026rsquo;s feasibility and laying the groundwork for transfer to near-industrial conditions. Initially validated under laboratory settings, the proposed method will be applied in a progressive forming tool to assess the impact of real-world disturbances such as dust and vibration. While the laboratory results are promising, further optimization of the measurement technique and data evaluation is required for full industrial implementation.\u003c/p\u003e","manuscriptTitle":"Automated non-destructive characterization for the cutting surfaces of punched holes using a laser profiler","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 09:22:00","doi":"10.21203/rs.3.rs-8609328/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-10T13:24:07+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T11:00:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T08:25:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"168467585090624717310880815749136704591","date":"2026-01-21T14:08:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8045128928825828154658502921567757614","date":"2026-01-21T12:44:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-19T11:03:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-16T09:23:43+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-16T05:55:39+00:00","index":"","fulltext":""},{"type":"submitted","content":"Production Engineering","date":"2026-01-15T09:56:31+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"production-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Production Engineering](https://www.springer.com/journal/11740)","snPcode":"11740","submissionUrl":"https://submission.nature.com/new-submission/11740/3","title":"Production Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"43467063-1c66-4b8f-abd6-cb102d007fc0","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-20T10:41:10+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 09:22:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8609328","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8609328","identity":"rs-8609328","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}