AI regression model based on effective abrasive force in MAF process for high-precision surface roughness prediction

AI regression model based on effective abrasive force in MAF process for high-precision surface roughness 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 AI regression model based on effective abrasive force in MAF process for high-precision surface roughness prediction Won-Jun Bae, Jung-Hee Lee, Jae-Seob Kwak This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7202082/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 22 Dec, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract Surface roughness is one of the most critical quality indicators in precision machining, as it directly affects product functionality, durability, and assemblability. Therefore, this study proposes a quantification method based on frequency response analysis to predict surface roughness improvement using abrasive force signals generated during the magnetic abrasive finishing (MAF) process. Raw abrasive force signals measured by the tool dynamometer were analyzed using fast Fourier transform (FFT) to identify the effective frequency band between 500 and 1,200 Hz. Subsequently, the real-time effective abrasive force was quantitatively extracted through inverse FFT (IFFT) and root mean square (RMS) calculations. The extracted effective force was used as an input variable in the AI-based second-order regression model, and its predictive performance was compared with a model using only process variables based on a total of 27 experimental datasets. As a result, the model incorporating real-time effective abrasive force demonstrated superior prediction accuracy with a coefficient of determination ( \(\:{R}^{2}\) ) of 0.9571 and a root mean square error (RMSE) of 0.0204. In addition, further validation experiments conducted under arbitrary conditions confirmed its high generalization capability with an average of 0.0117. Frequency response analysis Real-time abrasive force AI-based regression model Effective force extraction Surface roughness prediction Full Text Cite Share Download PDF Status: Published Journal Publication published 22 Dec, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 18 Oct, 2025 Reviewers agreed at journal 23 Aug, 2025 Reviewers invited by journal 23 Aug, 2025 Editor assigned by journal 27 Jul, 2025 First submitted to journal 23 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. 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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-7202082","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504618780,"identity":"870038ed-cbd2-4d1d-9211-5ab757d9f683","order_by":0,"name":"Won-Jun Bae","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Won-Jun","middleName":"","lastName":"Bae","suffix":""},{"id":504618781,"identity":"81cb3c87-344c-4261-9634-fef95ca09ee7","order_by":1,"name":"Jung-Hee Lee","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jung-Hee","middleName":"","lastName":"Lee","suffix":""},{"id":504618782,"identity":"b8233e58-3085-4725-828d-ec50eeea47f0","order_by":2,"name":"Jae-Seob Kwak","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIiWNgGAWjYBACxgY2IHngnxwDD1REgkgtB4yJ18LAANGS2EC0Fub+Y2kSP87cSd/Oc/aYBEONHYPk7AMEHDYj7Zhkz41nuTt7+9IkGI4lM0jzJRDSwt52g+cDc+6G8zxmEgxsBxjkePDrYGDsP952888H5nQDsJZ/xGhpSDt2m+fG4QSDsz1mEoxtBxikCWqZkZb+W+ZMmuHOnjPGFol9yTySPQS0GPYfMzZ8c8xG3pwnx/DGh292chJnCGlpgDIMQEQCAwMhZzEwyMMYBgSVjoJRMApGwYgFAEAJQqeQmPa7AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-2456-2000","institution":"Pukyong National University - Daeyeon Campus: Pukyong National University","correspondingAuthor":true,"prefix":"","firstName":"Jae-Seob","middleName":"","lastName":"Kwak","suffix":""}],"badges":[],"createdAt":"2025-07-24 06:25:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7202082/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7202082/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-17193-0","type":"published","date":"2025-12-22T15:58:02+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":99172337,"identity":"79a960f4-feec-40eb-9cad-421e7df0d6d8","added_by":"auto","created_at":"2025-12-29 16:08:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1316525,"visible":true,"origin":"","legend":"","description":"","filename":"AIregressionmodelbasedoneffectiveabrasiveforce.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7202082/v1_covered_2dae5a52-3173-4c38-9a23-595f7f1a14d6.pdf"}],"financialInterests":"","formattedTitle":"AI regression model based on effective abrasive force in MAF process for high-precision surface roughness prediction","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":true,"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":"Frequency response analysis, Real-time abrasive force, AI-based regression model, Effective force extraction, Surface roughness prediction","lastPublishedDoi":"10.21203/rs.3.rs-7202082/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7202082/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSurface roughness is one of the most critical quality indicators in precision machining, as it directly affects product functionality, durability, and assemblability. Therefore, this study proposes a quantification method based on frequency response analysis to predict surface roughness improvement using abrasive force signals generated during the magnetic abrasive finishing (MAF) process. Raw abrasive force signals measured by the tool dynamometer were analyzed using fast Fourier transform (FFT) to identify the effective frequency band between 500 and 1,200 Hz. Subsequently, the real-time effective abrasive force was quantitatively extracted through inverse FFT (IFFT) and root mean square (RMS) calculations. The extracted effective force was used as an input variable in the AI-based second-order regression model, and its predictive performance was compared with a model using only process variables based on a total of 27 experimental datasets. 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