Application of Pretrained Model for Zero Shot Tool Wear Monitoring with High Fidelity

Application of Pretrained Model for Zero Shot Tool Wear Monitoring with High Fidelity | 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 Application of Pretrained Model for Zero Shot Tool Wear Monitoring with High Fidelity Sakib Ul Islam, Hari krishna Kandasamy, Nelson Santos, Siyu Tu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6975138/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract To minimize CNC tool wear related non-compliance, wastage form premature tool replacement is a common practice in high precision low-volume high-mix machining environments. An in-line vision-based manufacturing process monitoring for tool wear is essential to effectively minimize such wastage. To date, a significant amount of work has been reported for off-line visual tool inspection using skillful classical image processing techniques. Lately, application-specific AI algorithms have demonstrated reliable wear detection with adequate image training. To overcome the limitations of classical and application specific AI algorithms, this application-oriented research article focuses on a fast, reliable, industry-ready, and easy-to-use in-line tool wear monitoring system. In this regard, this study investigates the capability of an open-sourced pretrained zero-shot image segmentation model for tool wear monitoring for the first time. Few versions of pre-trained deep learning Segment Anything Model (SAM) is studied for image segmentation. Then, an efficient tool wear monitoring process pipeline is established with great success for erosive tool wear in dry milling. With an automatic processing pipeline with a cycle time of only 0.572 seconds per image, a 3.81% relative error in gradual flank wear is observed. Moreover, different lighting conditions to mimic industrial environments are tested with a coefficient of variance of only 10%. In summary, by leveraging the pretrained SAM model, its zero-shot segmentation capabilities, and ease of implementation and adaptability; this method demonstrates a new approach towards manufacturing process monitoring. Cutting Tool Wear Machine Vision Deep Learning Pre-Trained model Zero Shot Inspection Milling Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revisions Needed 16 Sep, 2025 Reviewers agreed at journal 03 Jul, 2025 Reviewers invited by journal 03 Jul, 2025 Editor assigned by journal 03 Jul, 2025 First submitted to journal 02 Jul, 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. 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-6975138","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":480216436,"identity":"10724a83-e088-4606-a10e-74ee3ede455f","order_by":0,"name":"Sakib Ul Islam","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIie2PMUvDQBTHXyk0y5OsJ4bkKzzp4hDwq1wRziVCJseQLYNKt6Lgh+hHiBzUJY5Cwi1O7dJBcOmQwZdUKkKa4iZyP3h3/+H97t4DsFj+JCPQQOA1MedCcFK+6OyggltFcsDGJNGrDNvHWySXaA7YrwTZYqLjOEF3lg/00Sb03IfV63wTC3CzG9mlUKFyfU8ahZGgUSoO0XV1y4OJ4mXeqcBlqpFyhK3C+SRSJbJC4qpTCaarRkkw2CnHharqHgVKHgxpiLRThLMwfb9QuZTtLqdmkj49Kt6liEbGI4H7dgmmavwR14nvmwv9vg7Dczd7XlbrOvTd7K57sG8G6VdAas8D7T9w3n7TbbFYLP+fTwfZYX663SA7AAAAAElFTkSuQmCC","orcid":"","institution":"University of Western Ontario: Western University","correspondingAuthor":true,"prefix":"","firstName":"Sakib","middleName":"Ul","lastName":"Islam","suffix":""},{"id":480216437,"identity":"7d176217-72f0-4baa-895c-da4fa9e78747","order_by":1,"name":"Hari krishna Kandasamy","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Hari","middleName":"krishna","lastName":"Kandasamy","suffix":""},{"id":480216438,"identity":"e3200b58-aa86-4c1c-9a6a-76b34d0bdb6b","order_by":2,"name":"Nelson Santos","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Nelson","middleName":"","lastName":"Santos","suffix":""},{"id":480216439,"identity":"9406cd00-42b4-4e6a-84a9-3f8d58d31b23","order_by":3,"name":"Siyu Tu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Tu","suffix":""},{"id":480216440,"identity":"401191f8-c18c-4a47-89ef-ec038081329e","order_by":4,"name":"Mohammed Tauhiduzzaman","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Mohammed","middleName":"","lastName":"Tauhiduzzaman","suffix":""}],"badges":[],"createdAt":"2025-06-25 13:19:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6975138/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6975138/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":86156266,"identity":"3868718e-c00b-4465-b57e-cb6f9974c135","added_by":"auto","created_at":"2025-07-07 11:14:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1458518,"visible":true,"origin":"","legend":"","description":"","filename":"ZeroShotToolWearMonitoringwithHighFidelity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6975138/v1_covered_bf043a86-e9a5-4b88-a88c-65bdd9801911.pdf"}],"financialInterests":"","formattedTitle":"Application of Pretrained Model for Zero Shot Tool Wear Monitoring with High Fidelity","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Cutting Tool Wear, Machine Vision, Deep Learning, Pre-Trained model, Zero Shot, Inspection, Milling","lastPublishedDoi":"10.21203/rs.3.rs-6975138/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6975138/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"To minimize CNC tool wear related non-compliance, wastage form premature tool replacement is a common practice in high precision low-volume high-mix machining environments. 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