Quality Assessment and Fast Geometry Prediction in Paperboard Forming

Quality Assessment and Fast Geometry Prediction in Paperboard Forming | 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 Quality Assessment and Fast Geometry Prediction in Paperboard Forming Klara Liesegang, Patrick Link, Georg Steinert, Lena Berthold, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8038770/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted You are reading this latest preprint version Abstract Paperboard forming is a productive process that meets the increasing demands for recyclability in packaging. However, it remains underutilized in mass production due to open challenges, for instance volatile material behavior and control challenges. Moreover, wrinkles are inevitable during paperboard forming. To produce numerous small wrinkles that do not compromise part quality and can improve its stability, the process must be set up appropriately. Achieving this efficiently requires minimal testing, which is complicated by rapid variations in material properties due to external factors. One solution to improve process design and stabilize a running process is integrating machine learning (ML) models, which represent the forming process, into a stroke-to-stroke control. Therefore, this study presents an approach consisting of Finite Element (FE) simulations to generate data, an ML model trained on them, and quality assessment for the predicted parts with an optimization strategy to determine process parameters. FE simulations were built to represent the forming process and ML models were trained on their results to establish a fast correlation between material properties, press settings, and the resulting part geometry, including wrinkles. An automated quality assessment using Fast Fourier Transform quantifies the quality of the part in terms of wrinkles. Results show that the ML model is integrated into an optimization strategy to propose enhanced process parameters if quality deviations occur. The ML model accurately represents FE simulation results and collaborates with quality assessment and optimization fast enough to ensure high part quality in a forming process with cycles of up to 60 strokes per minute. Machine Learning Forming Paperboard FE Simulation Full Text Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → 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-8038770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":544123992,"identity":"dca4f7cd-5626-449f-b7ef-da7e93cd133f","order_by":0,"name":"Klara 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However, it remains underutilized in mass production due to open challenges, for instance volatile material behavior and control challenges. Moreover, wrinkles are inevitable during paperboard forming. To produce numerous small wrinkles that do not compromise part quality and can improve its stability, the process must be set up appropriately. Achieving this efficiently requires minimal\ntesting, which is complicated by rapid variations in material properties due to external factors. One solution to improve process design and stabilize a running process is integrating machine learning (ML) models, which represent the forming process, into a stroke-to-stroke control. Therefore, this study presents an approach consisting of Finite Element (FE) simulations to generate data, an ML model trained on them, and quality assessment for the predicted parts with an optimization strategy to determine process parameters. FE simulations were built to represent the forming process and ML models were trained on their results to establish a fast correlation between material properties, press settings, and the resulting part geometry, including wrinkles. An automated quality assessment using Fast Fourier Transform quantifies the quality of the part in terms of wrinkles. Results show that the ML model is integrated into an optimization strategy to propose enhanced process parameters if quality deviations occur. The ML model accurately represents FE simulation results and collaborates with quality assessment and optimization fast enough to ensure high part quality in a forming process\nwith cycles of up to 60 strokes per minute.","manuscriptTitle":"Quality Assessment and Fast Geometry Prediction in Paperboard Forming","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 09:54:46","doi":"10.21203/rs.3.rs-8038770/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6c59c1ec-9518-4741-b2a7-e597506c166d","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T16:05:53+00:00","versionOfRecord":{"articleIdentity":"rs-8038770","link":"https://doi.org/10.1007/s00170-026-17694-6","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-02-19 15:58:00","publishedOnDateReadable":"February 19th, 2026"},"versionCreatedAt":"2025-11-18 09:54:46","video":"","vorDoi":"10.1007/s00170-026-17694-6","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17694-6","workflowStages":[]},"version":"v1","identity":"rs-8038770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8038770","identity":"rs-8038770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}