Concept-Based Force-Signature Analysis of Tool-Parameter Effects in Fine Blanking

Concept-based force-signature analysis of tool-parameter effects in fine blanking | 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 Concept-based force-signature analysis of tool-parameter effects in fine blanking Martin Unterberg, Daria Gelbich, Frank Schweinshaupt, Antonia Holzapfel, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8730577/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Fine blanking part quality is highly sensitive to tool parameterization, yet cause–effect relationships are obscured by substantial process noise. This limits the utility of both simulation and scalar process metrics for tool optimization and mechanistic understanding. At the same time, high-resolution force-signature data is information-rich and can be used as evidence for learning physically plausible relationships, provided that interpretability is retained. This paper presents a data-to-knowledge pipeline combining finite element simulation, neural network prediction, and concept extraction to derive interpretable patterns from force time series and formulate mechanistic hypotheses linking tool parameters to die-roll formation. Controlled tool-parameter variation generates approximately 27,000 strokes with die-roll metrology. Neural networks predict die-roll height and tool parameters with mean absolute percentage errors of 0.6% and 2.6%, respectively. Concept extraction (ECLAD-ts) and segment-aware attribution localize predictive information to peak-load and post-peak regimes. For die-clearance variation, piecewise-linear analysis within concept windows identifies two mechanism-sensitive slope descriptors (Cliff's |δ| ≥ 0.83) with ordered progressions mirroring the graded die-roll response. The rising-flank slope captures clearance-dependent shearing resistance, while the post-peak decay slope reflects thermo-mechanical softening once crack growth initiates. Smaller clearance yields higher resistance, faster decay, and reduced die-roll height, consistent with forming theory. The pipeline enables phase-local assessment of tool-parameter effects under process noise and supports testable mechanistic hypotheses for simulation refinement. Fine blanking Process noise Explainable AI XAI Concept extraction Predictive quality Deep learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 2 posted You are reading this latest preprint version Show more versions 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-8730577","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":609608040,"identity":"e7ff99dc-88ec-4841-9c42-4035222dc767","order_by":0,"name":"Martin 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This limits the utility of both simulation and scalar process metrics for tool optimization and mechanistic understanding. At the same time, high-resolution force-signature data is information-rich and can be used as evidence for learning physically plausible relationships, provided that interpretability is retained. This paper presents a data-to-knowledge pipeline combining finite element simulation, neural network prediction, and concept extraction to derive interpretable patterns from force time series and formulate mechanistic hypotheses linking tool parameters to die-roll formation. Controlled tool-parameter variation generates approximately 27,000 strokes with die-roll metrology. Neural networks predict die-roll height and tool parameters with mean absolute percentage errors of 0.6% and 2.6%, respectively. Concept extraction (ECLAD-ts) and segment-aware attribution localize predictive information to peak-load and post-peak regimes. For die-clearance variation, piecewise-linear analysis within concept windows identifies two mechanism-sensitive slope descriptors (Cliff's |δ| ≥ 0.83) with ordered progressions mirroring the graded die-roll response. The rising-flank slope captures clearance-dependent shearing resistance, while the post-peak decay slope reflects thermo-mechanical softening once crack growth initiates. Smaller clearance yields higher resistance, faster decay, and reduced die-roll height, consistent with forming theory. 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