Camera-based feature extraction and uncertainty analysis in deep drawing in progressive dies

Camera-based feature extraction and uncertainty analysis in deep drawing in progressive dies | 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 Camera-based feature extraction and uncertainty analysis in deep drawing in progressive dies Markus Schumann, Jonas Moske, Antonia Wüst, Peter Groche This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7970217/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 In progressive die deep drawing, small deviations in strip positioning and transport can accumulate across stages, leading to significant geometric variations and quality issues. Reliable datasets are therefore essential for data-driven modeling, yet process labels such as the nominal infeed are often affected by machine tolerances and thus introduce uncertainty. This work presents a camera-based methodology for automated feature extraction at the end of the deep drawing process. Using an inline profile projector, relevant geometric features such as circle distances, centroid positions, and local material widths are extracted from high-speed image data. The implemented algorithm combines edge detection, circle fitting, centroid analysis, and directional width measurements to generate consistent feature sets under industrial conditions. Challenges such as material reflections, asymmetric deformation, and non-ideal boundary conditions are addressed, and geometry-specific obstacles for feature detection are discussed. The extracted features are further analyzed with respect to their statistical variability across systematically varied infeed values. Results demonstrate that the variance of image-based features increases with higher infeed, indicating that nominal process labels do not always reflect the actual material position. The study highlights the importance of uncertainty quantification in dataset generation for machine learning in forming technology. By linking process variation to directly observable geometry features, the proposed approach provides both methodological guidance for feature extraction and conceptual insights into label quality in industrial datasets. camera-based feature extraction uncertainty quantification progressive die deep drawing inline measurement data-driven modeling label noise Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 28 Nov, 2025 Reviews received at journal 28 Nov, 2025 Reviews received at journal 25 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 05 Nov, 2025 Reviewers invited by journal 03 Nov, 2025 Editor assigned by journal 29 Oct, 2025 Submission checks completed at journal 29 Oct, 2025 First submitted to journal 28 Oct, 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-7970217","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":537105428,"identity":"7380b7af-88b2-4b53-83b0-cc7e49fdfe17","order_by":0,"name":"Markus 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