A grinding wheel wear state prediction method based on adaptive-weighted decision-level fusion for industrial production

A grinding wheel wear state prediction method based on adaptive-weighted decision-level fusion for industrial production | 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 A grinding wheel wear state prediction method based on adaptive-weighted decision-level fusion for industrial production Rao Li, Zhihang Li, Longlong Li, Shiyao Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8219851/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 Accurate prediction of grinding wheel wear is crucial for ensuring grinding quality, improving production efficiency, and advancing the intelligence of manufacturing processes. However, the wear evolution of grinding wheels is influenced by machining conditions, material properties, and dynamic process characteristics, resulting in complex nonlinear behaviour that is difficult to measure directly. To address these challenges in practical industrial scenarios, this study first analyses the evolution relationship between grinding wheel wear and workpiece dimensional accuracy, and proposes an indirect wear characterization method based on vibration signals to enable rapid mapping of wear information. Furthermore, considering that vibration signals collected from different positions contribute unequally to wear characterization, a multi-scale convolutional neural network with adaptive-weighted ensemble learning (EWMCNN) is developed. The model extracts multi-band vibration features through a multi-scale convolutional architecture and employs a meta-learner to dynamically assess the importance of features from different positions, thereby achieving optimal decision-level fusion through adaptive weight allocation. Finally, based on a grinding vibration data acquisition platform and incorporating discrete wavelet transform denoising and data augmentation, the proposed method enables dynamic prediction of grinding wheel wear. Ablation experiments demonstrate that the proposed EWMCNN achieves superior prediction accuracy and generalization capability compared with other baseline models, providing an efficient and reliable solution for online monitoring of grinding wheel wear. Manufacturing Intelligence Grinding Wheel Wear Ensemble Learning Multi-Sensor Feature Fusion Full Text Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor Revisions Needed 30 Apr, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers invited by journal 08 Feb, 2026 Editor assigned by journal 30 Nov, 2025 First submitted to journal 26 Nov, 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-8219851","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587642782,"identity":"fbe0f1a1-e33c-4116-90f5-3d9bcbd1f177","order_by":0,"name":"Rao Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Rao","middleName":"","lastName":"Li","suffix":""},{"id":587642783,"identity":"f4c1bd52-d968-41f5-9579-3506723e8874","order_by":1,"name":"Zhihang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8UlEQVRIiWNgGAWjYBACPmYGNobEBiCLvQEmloBfCxtcC88BEN+ACC0gxAjSIpFArBZ29mcPHu6wy5OPfPzwxo+KPwz87DkGDD934HVYukHimeRiw9tpxpY9ZwwYJHveGDD2nsGr5ZhEYhtz4sbZOWzSjG0GDAY3cgyYGdvwaWFsA2qpT9w48wxQyz8DBnvCWpjZgFoOJ86X4AFqaQDaIkFQCxtIy/HEDTwgvxwz5pE486zgYC8eLfz8x59J/myrTpzffhgYYjVycvztyRsf/MSjBQ4MDgCjBkjzgDgHiNDAwCDfANEyCkbBKBgFowADAAC1wEivcmUHogAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0697-6680","institution":"Chongqing College of Electronic Engineering","correspondingAuthor":true,"prefix":"","firstName":"Zhihang","middleName":"","lastName":"Li","suffix":""},{"id":587642784,"identity":"d883e31a-a7c6-486f-84a5-5f08a8d757e5","order_by":2,"name":"Longlong Li","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Longlong","middleName":"","lastName":"Li","suffix":""},{"id":587642785,"identity":"ab096c84-e30f-476e-aff6-9f30586cc36b","order_by":3,"name":"Shiyao Fu","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Shiyao","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2025-11-27 08:52:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8219851/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8219851/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102748033,"identity":"048f566f-d2bd-4ead-9e50-291583ab46b2","added_by":"auto","created_at":"2026-02-16 09:05:47","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1324163,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptSubmissionv2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8219851/v1_covered_7d25c534-e299-4d3d-8fb6-318268b21f1d.pdf"}],"financialInterests":"","formattedTitle":"A grinding wheel wear state prediction method based on adaptive-weighted decision-level fusion for industrial production","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":"Manufacturing Intelligence, Grinding Wheel Wear, Ensemble Learning, Multi-Sensor, Feature Fusion","lastPublishedDoi":"10.21203/rs.3.rs-8219851/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8219851/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurate prediction of grinding wheel wear is crucial for ensuring grinding quality, improving production efficiency, and advancing the intelligence of manufacturing processes. However, the wear evolution of grinding wheels is influenced by machining conditions, material properties, and dynamic process characteristics, resulting in complex nonlinear behaviour that is difficult to measure directly. To address these challenges in practical industrial scenarios, this study first analyses the evolution relationship between grinding wheel wear and workpiece dimensional accuracy, and proposes an indirect wear characterization method based on vibration signals to enable rapid mapping of wear information. Furthermore, considering that vibration signals collected from different positions contribute unequally to wear characterization, a multi-scale convolutional neural network with adaptive-weighted ensemble learning (EWMCNN) is developed. 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