{"paper_id":"10df25cf-1638-41af-baa3-9d1a791f53e8","body_text":"Digital twin-based tool wear monitoring of micro-milling process | 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 Digital twin-based tool wear monitoring of micro-milling process Ziming Wang, Zhiyi Zhang, Yuchen Zhao, Zhonghao Zhu, Fanmao Zeng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6287015/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract In-situ prediction and intelligent monitoring of micro-milling tool wear remain challenging problems in micro-milling technology. Digital twin technology offers a promising solution to address these issues. This study proposes a digital twin-based monitoring system for micro-milling tool wear. First, micro-milling tool wear experiments were conducted, with the tool diameter reduction rate and flank wear land width used as evaluation indicators. An intelligent prediction model based on a Long Short-Term Memory (LSTM) network was developed to achieve high-precision forecasting of tool wear. Subsequently, the overall architecture design of the digital twin-based monitoring system was completed. A micro-milling motion simulation model was established to enable real-time acquisition, preprocessing, and transmission of micro-milling force data. Leveraging the Unity 3D software development platform and C# programming language, a digital twin-based micro-milling tool wear monitoring system was developed. This system realizes twin reproduction of the micro-milling process and tool wear monitoring, providing critical support for intuitive visualization of the micro-milling process and accurate assessment of tool wear status. Micro-milling Tool wear Digital twin Micro-milling tool wear monitoring system Full Text Cite Share Download PDF Status: Published Journal Publication published 16 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 22 Jun, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 26 Mar, 2025 First submitted to journal 24 Mar, 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. 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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-6287015\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":435209766,\"identity\":\"1fdb8381-53a4-4628-a066-fbfdfcd728c9\",\"order_by\":0,\"name\":\"Ziming Wang\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Ziming\",\"middleName\":\"\",\"lastName\":\"Wang\",\"suffix\":\"\"},{\"id\":435209767,\"identity\":\"916fa54a-eb4c-4a6a-8770-16902fe6118c\",\"order_by\":1,\"name\":\"Zhiyi 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Digital twin technology offers a promising solution to address these issues. This study proposes a digital twin-based monitoring system for micro-milling tool wear. First, micro-milling tool wear experiments were conducted, with the tool diameter reduction rate and flank wear land width used as evaluation indicators. An intelligent prediction model based on a Long Short-Term Memory (LSTM) network was developed to achieve high-precision forecasting of tool wear. Subsequently, the overall architecture design of the digital twin-based monitoring system was completed. A micro-milling motion simulation model was established to enable real-time acquisition, preprocessing, and transmission of micro-milling force data. Leveraging the Unity 3D software development platform and C# programming language, a digital twin-based micro-milling tool wear monitoring system was developed. 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