End milling tool condition monitoring with vibration signals and machine learning classifiers: A comparative study

End milling tool condition monitoring with vibration signals and machine learning classifiers: A comparative study | 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 End milling tool condition monitoring with vibration signals and machine learning classifiers: A comparative study Nilesh Dhobale, Pradipkumar Darade, Amol Bhanage, Nilesh Satonkar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7455892/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Contemporary industrial businesses are encountering issues related to tool wear. To address this issue, the implementation of tool condition monitoring (TCM) is imperative, necessitating the development of such solutions. These technologies will help improve the tool's product quality and life and ultimately reduce the tool wear. The present article focuses on multipoint milling tool supervision (Health Monitoring of End Milling Tool) using a Machine Learning approach. In this experimentation, vibration signature data has collected for the multipoint milling tool, extracted vibration signatures (signals) have further used for statistical feature extraction. The J48 algorithm's decision tree have taken to select the best features. The fault classification has carried with a different classifier such as Logit Boost, linear regression, Simple Logistic, and Bagged trees classifier. Comparison of these classifiers is carried out based on vibration signatures, and out of these classifiers, the Logit Boost classifier gives better classification and accuracy than other classifiers. In the Logit Boost, linear regression, Simple Logistic, and Bagged trees classifier, maximum classification accuracy was 95.83%, 95%, 94.38%, and 94.54%, respectively, with a selection of the best statistical features. Similarly, classification accuracy was 96.25%, 95.2083%, 93.75%, and 92.7083% without selecting the best features. The wavelet analysis has been used to compare maximum classification accuracy with statistical feature extraction.In this Daubechies, Symlets, Haar and Discrete Meyer wavelet, Coiflet wavelet family classification accuracy have obtained. In the Daubechies wavelet family, utmost classification accuracy is 99.38% with db5 wavelet, and in the symlets wavelet family, utmost classification accuracy has received with a simple logistic classifier, i.e., 99.17% with sym5 wavelet. Tool condition monitoring Vibration Signals Statistical and Wavelet Extraction Machine Learning classifiers Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revisions 15 Jan, 2026 Reviewers agreed at journal 30 Sep, 2025 Reviewers invited by journal 30 Sep, 2025 Editor invited by journal 16 Sep, 2025 Editor assigned by journal 27 Aug, 2025 First submitted to journal 25 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. 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-7455892","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":522803551,"identity":"eabfa8d6-92ff-4004-a1ee-0f0326680d0d","order_by":0,"name":"Nilesh 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study","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":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and 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To address this issue, the implementation of tool condition monitoring (TCM) is imperative, necessitating the development of such solutions. These technologies will help improve the tool's product quality and life and ultimately reduce the tool wear. The present article focuses on multipoint milling tool supervision (Health Monitoring of End Milling Tool) using a Machine Learning approach. In this experimentation, vibration signature data has collected for the multipoint milling tool, extracted vibration signatures (signals) have further used for statistical feature extraction. The J48 algorithm's decision tree have taken to select the best features. The fault classification has carried with a different classifier such as Logit Boost, linear regression, Simple Logistic, and Bagged trees classifier. Comparison of these classifiers is carried out based on vibration signatures, and out of these classifiers, the Logit Boost classifier gives better classification and accuracy than other classifiers. In the Logit Boost, linear regression, Simple Logistic, and Bagged trees classifier, maximum classification accuracy was 95.83%, 95%, 94.38%, and 94.54%, respectively, with a selection of the best statistical features. Similarly, classification accuracy was 96.25%, 95.2083%, 93.75%, and 92.7083% without selecting the best features. The wavelet analysis has been used to compare maximum classification accuracy with statistical feature extraction.In this Daubechies, Symlets, Haar and Discrete Meyer wavelet, Coiflet wavelet family classification accuracy have obtained. In the Daubechies wavelet family, utmost classification accuracy is 99.38% with db5 wavelet, and in the symlets wavelet family, utmost classification accuracy has received with a simple logistic classifier, i.e., 99.17% with sym5 wavelet.\u003c/p\u003e","manuscriptTitle":"End milling tool condition monitoring with vibration signals and machine learning classifiers: A comparative study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-13 02:23:22","doi":"10.21203/rs.3.rs-7455892/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revisions","date":"2026-01-16T03:24:10+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2025-09-30T11:10:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-30T10:33:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"International Journal of System Assurance Engineering and Management","date":"2025-09-16T14:17:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-27T13:17:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"International Journal of System Assurance Engineering and Management","date":"2025-08-25T13:02:23+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"525451c4-61dd-494c-bcea-d8dc26a51745","owner":[],"postedDate":"October 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-03-04T08:49:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-13 02:23:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7455892","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7455892","identity":"rs-7455892","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}