Improved depth residual network based tool wear prediction for cavity milling process

Improved depth residual network based tool wear prediction for cavity 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 Improved depth residual network based tool wear prediction for cavity milling process zhiwei guan, Junyu Cong, Fei Wang, Guofeng Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3024533/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Dec, 2023 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 4 You are reading this latest preprint version Abstract The parts with integrated design technology are widely used in aerospace field because of their advantages such as high strength and high reliability compared with riveted structures, which often present multiple cavities and thin walls, and milling occupies a large amount of machining time, while it is very easy to cause rapid degradation of tool performance because their materials are mostly made of hard-to-machine metals. To address the failure of traditional methods in monitoring the tool trajectory of complex cavity milling process, this paper proposes a tool wear prediction method based on short-time Fourier transform and improved depth residual network. Firstly, the short-time Fourier transform is used to convert the signal into a time-frequency map. Then, to solve the problem that the depth residual network describes the machining state from a single perspective, the original model is improved by adding a feature fusion layer. Finally, the signal and the time-frequency map are simultaneously input into the improved deep residual network, and the tool wear sensitive features filtered by Pearson correlation coefficient are extracted from the time domain and frequency domain of the signal, and the deep features in the time-frequency domain are extracted from the residual block structure of the time-frequency map, and the tool wear sensitive features and the deep features in the time-frequency domain are fused in the feature fusion layer to complete the model training. The experimental results show that the average prediction deviation of tool wear by the regression model established in this paper is 0.76%, which is lower than that of the original deep residual network, shallow convolutional neural network and artificial feature-based machine learning model. Cavity milling Tool wear prediction Short time Fourier transform Improved depth residual network Full Text Cite Share Download PDF Status: Published Journal Publication published 13 Dec, 2023 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviewers agreed at journal 09 Jun, 2023 Reviewers invited by journal 09 Jun, 2023 Editor assigned by journal 09 Jun, 2023 First submitted to journal 05 Jun, 2023 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-3024533","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":208390006,"identity":"c618d065-12aa-4f72-949c-befbb928707b","order_by":0,"name":"zhiwei guan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIie3RsWoCQRCA4VkGZpvVa1fOVwiMCGJx+CwXFraysEwRcEHQ5h7gIAFfwTdwyZGrBNsUqVOflQZCyPWRW9Ol2L+eb2AYgFjsP4bJ+bN5eESxPZ4vDWezMJHAqT7UEkvgQbmwJkwSYN1bYyJbkqrmRbiQuFtBzpoo7T25fJyxR5DV666LTCrw+UINx4Nn782c3/ugrH3rJsJ5rcmM3L2r5vyBoNUkQFA4xbjceyNWU243hAkhqBxRlBYRbiOKUPsaRXEgUbA1FLzl2E6evttXbgqCy1c2S2RVd5Lf0d/GY7FYLHatH3uHRyuHrqMHAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"zhiwei","middleName":"","lastName":"guan","suffix":""},{"id":208390007,"identity":"96541caa-4669-421b-8609-065d36a72925","order_by":1,"name":"Junyu Cong","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Junyu","middleName":"","lastName":"Cong","suffix":""},{"id":208390008,"identity":"bbcf255a-def4-4c61-a9dd-7a3870e37252","order_by":2,"name":"Fei Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Wang","suffix":""},{"id":208390009,"identity":"3ed0a0c2-e549-4ba7-a887-84e18a5000e6","order_by":3,"name":"Guofeng Wang","email":"","orcid":"","institution":"","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Guofeng","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2023-06-05 12:29:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3024533/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3024533/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-023-12829-5","type":"published","date":"2023-12-13T15:01:41+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":48401412,"identity":"f1fcff55-3433-4407-9ee1-350b1dc15712","added_by":"auto","created_at":"2023-12-18 15:09:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1494552,"visible":true,"origin":"","legend":"","description":"","filename":"manuscripts.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3024533/v1_covered_79355d96-b20e-48cb-b99a-0c373fdd1678.pdf"}],"financialInterests":"","formattedTitle":"Improved depth residual network based tool wear prediction for cavity milling process","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"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},"keywords":"Cavity milling, Tool wear prediction, Short time Fourier transform, Improved depth residual network","lastPublishedDoi":"10.21203/rs.3.rs-3024533/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3024533/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe parts with integrated design technology are widely used in aerospace field because of their advantages such as high strength and high reliability compared with riveted structures, which often present multiple cavities and thin walls, and milling occupies a large amount of machining time, while it is very easy to cause rapid degradation of tool performance because their materials are mostly made of hard-to-machine metals. 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