Hybrid Ultrasonic-Assisted Milling with Internal Cooling and Ensemble Machine Learning for Cutting-Tool Wear Prediction in SKD11 Hardened Steel

Hybrid Ultrasonic-Assisted Milling with Internal Cooling and Ensemble Machine Learning for Cutting-Tool Wear Prediction in SKD11 Hardened Steel | 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 Hybrid Ultrasonic-Assisted Milling with Internal Cooling and Ensemble Machine Learning for Cutting-Tool Wear Prediction in SKD11 Hardened Steel Shen Yung Lin, Zahid Abbas Shah, Hong-Xuan Peng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8559711/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Mar, 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 Machining of SKD11 tool steel challenging task due to high hardness and strength of material, which are responsible for accelerated cutting tool wear, high cutting temperature, and poor surface finish. The traditional cooling system is not able to control the heat generation process at higher speeds in an effective manner. Also, the previous research work related to Ultrasonic-Assisted milling has focused on restricted scope. This study work is concentrated on the development of an improved cutting strategy which enhances the life of the cutting tool and the quality of the surface. This work approach combines Ultrasonic-Assisted milling with through-tool internal cooling method. The ultrasonic vibration generates a high-frequency, low-amplitude motion which reduces the friction and improves chip removal. However, the internal cooling performs coolant injection directly into the cutting zone for better heat dissipation. Comparative experimental tests between Z-axis, XY-axis, and XYZ-axis ultrasonic mode assisted milling under different cooling conditions proved that the best results were obtained by Z-axis ultrasonic assistance and internal cooling: surface roughness was decreased by 20–31%, and the temperature by 15–28%, as compared to conventional milling. Various machine learning (ML) models, such as Decision Tree, Random Forest, and AdaBoost, are used to predict cutting tool wear. Among these, AdaBoost provides the highest accuracy and precision values i.e., 0.94 and 0.964286 respectively, which is more improved by a simple ensemble vote. This integrated process and predictive model offer a practical solution to improve the machinability of SKD11 and enable smarter, more sustainable manufacturing. Cutting tool wear prediction Machine-Learning Ultrasonic-Assisted milling Internal Cooling Full Text Cite Share Download PDF Status: Published Journal Publication published 30 Mar, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 13 Feb, 2026 Reviewers agreed at journal 13 Jan, 2026 Reviewers invited by journal 13 Jan, 2026 Editor assigned by journal 13 Jan, 2026 First submitted to journal 09 Jan, 2026 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-8559711","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":573990497,"identity":"495dcd45-39a7-41f4-b915-7dda3dcbcd82","order_by":0,"name":"Shen Yung 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Steel","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":"Cutting tool wear prediction, Machine-Learning, Ultrasonic-Assisted milling, Internal Cooling","lastPublishedDoi":"10.21203/rs.3.rs-8559711/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8559711/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMachining of SKD11 tool steel challenging task due to high hardness and strength of material, which are responsible for accelerated cutting tool wear, high cutting temperature, and poor surface finish. The traditional cooling system is not able to control the heat generation process at higher speeds in an effective manner. Also, the previous research work related to Ultrasonic-Assisted milling has focused on restricted scope. This study work is concentrated on the development of an improved cutting strategy which enhances the life of the cutting tool and the quality of the surface. This work approach combines Ultrasonic-Assisted milling with through-tool internal cooling method. The ultrasonic vibration generates a high-frequency, low-amplitude motion which reduces the friction and improves chip removal. However, the internal cooling performs coolant injection directly into the cutting zone for better heat dissipation. Comparative experimental tests between Z-axis, XY-axis, and XYZ-axis ultrasonic mode assisted milling under different cooling conditions proved that the best results were obtained by Z-axis ultrasonic assistance and internal cooling: surface roughness was decreased by 20\u0026ndash;31%, and the temperature by 15\u0026ndash;28%, as compared to conventional milling. Various machine learning (ML) models, such as Decision Tree, Random Forest, and AdaBoost, are used to predict cutting tool wear. Among these, AdaBoost provides the highest accuracy and precision values i.e., 0.94 and 0.964286 respectively, which is more improved by a simple ensemble vote. This integrated process and predictive model offer a practical solution to improve the machinability of SKD11 and enable smarter, more sustainable manufacturing.\u003c/p\u003e","manuscriptTitle":"Hybrid Ultrasonic-Assisted Milling with Internal Cooling and Ensemble Machine Learning for Cutting-Tool Wear Prediction in SKD11 Hardened Steel","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 15:21:29","doi":"10.21203/rs.3.rs-8559711/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major Revisions Needed","date":"2026-02-13T08:46:43+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2026-01-13T15:17:32+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T14:25:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-13T05:18:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"The International Journal of Advanced Manufacturing Technology","date":"2026-01-09T05:10:59+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":"1bc8bb25-c75b-4376-b4bd-4d468d611961","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T16:02:15+00:00","versionOfRecord":{"articleIdentity":"rs-8559711","link":"https://doi.org/10.1007/s00170-026-17951-8","journal":{"identity":"the-international-journal-of-advanced-manufacturing-technology","isVorOnly":false,"title":"The International Journal of Advanced Manufacturing Technology"},"publishedOn":"2026-03-30 15:58:54","publishedOnDateReadable":"March 30th, 2026"},"versionCreatedAt":"2026-01-16 15:21:29","video":"","vorDoi":"10.1007/s00170-026-17951-8","vorDoiUrl":"https://doi.org/10.1007/s00170-026-17951-8","workflowStages":[]},"version":"v1","identity":"rs-8559711","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8559711","identity":"rs-8559711","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}