Condition-Adaptive 1DCNN-Transformer for Multi-Condition Tool Wear Prediction

Condition-Adaptive 1DCNN-Transformer for Multi-Condition Tool Wear Prediction | 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 Condition-Adaptive 1DCNN-Transformer for Multi-Condition Tool Wear Prediction Nan zhang, Long Li, Ze Yu, Lin Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7225509/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 17 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 Accurate real-time prediction of tool wear under multi-condition operations is critical for ensuring machining precision and industrial productivity. This study proposes an end-to-end tool wear prediction framework integrating a 1D Convolutional Neural Network (1DCNN) and Transformer architecture, enhanced by dynamic parameter adaptation and transfer learning strategies. The core innovation lies in two aspects: 1) A novel Conditional Layer Normalization (CLN) mechanism that dynamically adjusts normalization parameters based on operational conditions (cutting depth, feed rate, material), enabling effective feature alignment across diverse scenarios; 2) A hybrid 1DCNN-Transformer structure that synergizes local temporal feature extraction through dilated convolutions with global dependency modeling via multi-head attention. Transfer learning is systematically implemented by freezing backbone parameters while fine-tuning task-specific layers, ensuring efficient knowledge transfer from single-condition pre-trained models to multi-condition applications. The model was rigorously validated through two complementary approaches: NASA milling dataset analysis (16 operational conditions) and titanium alloy TC17 machining experiments. Quantitative evaluations demonstrated superior performance, achieving R² scores of 0.9069, 0.9058and 0.9105 in material-variation, cutting-depth variation and cutting-speed adaptation tasks, respectively, with RMSE consistently below 0.0936. These results confirm the framework's capability to establish robust signal-to-wear mapping relationships under heterogeneous conditions. The proposed condition-aware architecture provides a scalable solution for industrial tool health monitoring systems. Tool wear prediction Deep learning Multiple conditions Transformer 1DCNN Full Text Cite Share Download PDF Status: Published Journal Publication published 17 Jan, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 30 Sep, 2025 Reviewers agreed at journal 22 Sep, 2025 Reviewers invited by journal 22 Sep, 2025 Editor assigned by journal 12 Aug, 2025 First submitted to journal 09 Aug, 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-7225509","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":518642675,"identity":"942e768c-40ea-40d1-84e6-c34f7d303dab","order_by":0,"name":"Nan 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Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Tool wear prediction, Deep learning, Multiple conditions, Transformer, 1DCNN","lastPublishedDoi":"10.21203/rs.3.rs-7225509/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7225509/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate real-time prediction of tool wear under multi-condition operations is critical for ensuring machining precision and industrial productivity. This study proposes an end-to-end tool wear prediction framework integrating a 1D Convolutional Neural Network (1DCNN) and Transformer architecture, enhanced by dynamic parameter adaptation and transfer learning strategies. The core innovation lies in two aspects: 1) A novel Conditional Layer Normalization (CLN) mechanism that dynamically adjusts normalization parameters based on operational conditions (cutting depth, feed rate, material), enabling effective feature alignment across diverse scenarios; 2) A hybrid 1DCNN-Transformer structure that synergizes local temporal feature extraction through dilated convolutions with global dependency modeling via multi-head attention. Transfer learning is systematically implemented by freezing backbone parameters while fine-tuning task-specific layers, ensuring efficient knowledge transfer from single-condition pre-trained models to multi-condition applications. The model was rigorously validated through two complementary approaches: NASA milling dataset analysis (16 operational conditions) and titanium alloy TC17 machining experiments. Quantitative evaluations demonstrated superior performance, achieving R² scores of 0.9069, 0.9058and 0.9105 in material-variation, cutting-depth variation and cutting-speed adaptation tasks, respectively, with RMSE consistently below 0.0936. These results confirm the framework's capability to establish robust signal-to-wear mapping relationships under heterogeneous conditions. The proposed condition-aware architecture provides a scalable solution for industrial tool health monitoring systems.\u003c/p\u003e","manuscriptTitle":"Condition-Adaptive 1DCNN-Transformer for Multi-Condition Tool Wear Prediction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-03 03:56:00","doi":"10.21203/rs.3.rs-7225509/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2025-09-30T23:08:30+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-22T10:20:15+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-22T10:10:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T08:17:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2025-08-09T04:57:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-international-journal-of-advanced-manufacturing-technology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jamt","sideBox":"Learn more about [The International Journal of Advanced Manufacturing Technology](https://www.springer.com/journal/170)","snPcode":"170","submissionUrl":"https://submission.nature.com/new-submission/170/3","title":"The International Journal of Advanced Manufacturing Technology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"2bc220c6-1785-465e-9658-f0b59842e161","owner":[],"postedDate":"October 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:46:26+00:00","versionOfRecord":{"articleIdentity":"rs-7225509","link":"https://doi.org/10.1007/s00170-026-17412-2","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-01-17 16:29:42","publishedOnDateReadable":"January 17th, 2026"},"versionCreatedAt":"2025-10-03 03:56:00","video":"","vorDoi":"10.1007/s00170-026-17412-2","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17412-2","workflowStages":[]},"version":"v1","identity":"rs-7225509","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7225509","identity":"rs-7225509","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}