Physics-driven Digital Twin Based Architecture of Wire Arc Additive Manufacturing Enabled by Fast Surrogate Calculations

Physics-driven Digital Twin Based Architecture of Wire Arc Additive Manufacturing Enabled by Fast Surrogate Calculations | 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 Physics-driven Digital Twin Based Architecture of Wire Arc Additive Manufacturing Enabled by Fast Surrogate Calculations Petro Pavlenko, Xuezhi Shi, Jinbao Wang, Oleh Makhnenko, Oleksii Milenin, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8542145/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This study presents physics-driven digital twin architecture for wire arc additive manufacturing, designed to integrate high-fidelity physical simulation with data-driven surrogate models for real-time-capable process monitoring and decision support. The proposed multilayer framework defines the interaction between physical, digital and control domains through standardized data exchange and scalable computational workflows. Core components, including finite-element thermal modeling, surrogate training and middleware integration, are developed and quantitatively validated to demonstrate their feasibility within the intended control loop. The obtained results confirm the technical readiness and efficiency of the proposed architecture as a foundation for next-generation intelligent manufacturing systems. digital twin wire arc additive manufacturing surrogate calculation finite element modeling neural networks monitoring Full Text Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 11 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 09 Jan, 2026 First submitted to journal 07 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-8542145","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589834558,"identity":"149b3c89-a48f-42aa-800f-75828c02fd38","order_by":0,"name":"Petro Pavlenko","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Petro","middleName":"","lastName":"Pavlenko","suffix":""},{"id":589834559,"identity":"50df76cf-615d-43d5-8b27-12c5ab67fea8","order_by":1,"name":"Xuezhi 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