Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies

Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies | 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 Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies Markus Schumann, Jonas Moske, Antonia Wüst, Felix Divo, Daria Gelbich, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8561321/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 use of machine learning (ML) in manufacturing requires structured, especially standardized, access to both simulation data and domain knowledge. This paper introduces a JSON-based data format for representing synthetic force-time series alongside expert annotations. The schema captures simulation metadata, tool and material parameters, and allows explicit expert knowledge, such as failure indicators, to be linked to signal segments.The proposed structure enables process-aware ML methods that leverage both domain knowledge and raw data for improved learning and generalization. A deep drawing use case illustrates how the format facilitates knowledge-guided learning. The approach aims to bridge the gap between real and simulated production data, supporting scalable integration in modern manufacturing systems. Synthetic process data Expert annotation Simulation metadata Knowledge representation Machine learning in manufacturing 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 09 Feb, 2026 Reviews received at journal 24 Jan, 2026 Reviewers agreed at journal 19 Jan, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 12 Jan, 2026 Editor assigned by journal 12 Jan, 2026 Submission checks completed at journal 10 Jan, 2026 First submitted to journal 09 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-8561321","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573385070,"identity":"0e2dac43-3947-4dec-8ff7-b105a04a78ab","order_by":0,"name":"Markus 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Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Synthetic process data, Expert annotation, Simulation metadata, Knowledge representation, Machine learning in manufacturing","lastPublishedDoi":"10.21203/rs.3.rs-8561321/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8561321/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe use of machine learning (ML) in manufacturing requires structured, especially standardized, access to both simulation data and domain knowledge. This paper introduces a JSON-based data format for representing synthetic force-time series alongside expert annotations. The schema captures simulation metadata, tool and material parameters, and allows explicit expert knowledge, such as failure indicators, to be linked to signal segments.The proposed structure enables process-aware ML methods that leverage both domain knowledge and raw data for improved learning and generalization. A deep drawing use case illustrates how the format facilitates knowledge-guided learning. The approach aims to bridge the gap between real and simulated production data, supporting scalable integration in modern manufacturing systems.\u003c/p\u003e","manuscriptTitle":"Structured Representation of Simulation and Annotation Data for Machine Learning in Forming Technologies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-13 22:23:50","doi":"10.21203/rs.3.rs-8561321/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-10T08:32:11+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-09T16:40:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-25T03:01:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"112323537637560877167625787916952309132","date":"2026-01-19T10:04:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"262617054460159852930420657202951401719","date":"2026-01-13T12:10:07+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-12T08:57:12+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-12T08:28:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-10T13:45:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Production Engineering","date":"2026-01-09T13:01:31+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":"7b3d1485-5fcf-4360-b9be-1a4fe677b81c","owner":[],"postedDate":"January 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-26T15:09:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-13 22:23:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8561321","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8561321","identity":"rs-8561321","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}