Burn risk modelling and cost estimation for surface grinding optimization

Burn risk modelling and cost estimation for surface grinding optimization | 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 Burn risk modelling and cost estimation for surface grinding optimization Taiwo D Fasae, Richard J Povinelli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5960362/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract Achieving zero defects in surface grinding cost efficiency requires selecting the right parameters that minimize the likelihood of thermal damage, along with the understanding that parameter choices are reflective of these likelihoods. However, the traditional approach to defect minimization uses constraint-based optimization, whereby a thermal model delineates parameter ranges that pose significant thermal risk. This assumes that thermal damage is binary (i.e., either it burns or does not) and therefore a qualitative rather than a quantitative approach to thermal damage avoidance. While the qualitative constraint approach has been effective, there are limitations on cost efficiency. This paper introduces the concept of burn cost analysis in grinding optimization. The proposed burn cost model is based on probability theory and estimation of burn risk and costs of thermal damage. Including burn costs in the total cost aligns with cost efficiency analysis in practice, to minimize the true costs of grinding including thermal damage costs. Our experimental setup involves a horizontal spindle reciprocating table surface grinder equipped with a 350 mm diameter cubic boron nitride (cBN) wheel. Grinding 2 mm downward cut depth on 85 mm x 203 mm x 11 mm Inconel 718 steel workpieces are performed. We demonstrate that the burn cost model inspires solutions that consider thermal damage risk quantitatively rather than qualitatively as done by the burn constraint model. In addition, considering the cost of the workpiece enables the burn cost model to capture a value-driven cost analysis perspective that is not previously seen. surface grinding thermal damage grinding burn burn detection optimization probability Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 May, 2025 Reviews received at journal 22 May, 2025 Reviewers agreed at journal 18 May, 2025 Reviewers agreed at journal 15 May, 2025 Reviews received at journal 14 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 13 May, 2025 Reviewers agreed at journal 17 Mar, 2025 Reviews received at journal 02 Mar, 2025 Reviews received at journal 01 Mar, 2025 Reviews received at journal 28 Feb, 2025 Reviewers agreed at journal 27 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers agreed at journal 24 Feb, 2025 Reviewers invited by journal 24 Feb, 2025 Editor assigned by journal 19 Feb, 2025 Submission checks completed at journal 13 Feb, 2025 First submitted to journal 04 Feb, 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. 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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-5960362","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":447977871,"identity":"489740b2-2b03-4ef0-9ec2-fe8ef406b4f1","order_by":0,"name":"Taiwo D Fasae","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABEklEQVRIiWNgGAWjYFACxgYgIQdmPcAQxwZ4IFLGIDazAUSMGSyERwsYgLWwSRClxZ79cJvEBwYDefn2w8eqbtTck91wvP/4gw8MNrIbDuCwhSexTXIGg4HhhjNpabdzjhUbbzhzmLFxBkOaMU4tDIltt3kY/jBuYMgxu53DlpC44UYyYzMPw+FEnFr4H4K0GNjP73//rTjnH1DL/ceMzX8Y/uPWIgG2xSCx4UYOG3NuG8gWZsZmBoYDuLXceNj+c4aBQfKGG8+MpXP7Eoxnnkk2nNljkGw8E4cW9v70xwYfKgxs5/cnP/yc8y1Btu/4wQcfflTYyfbh0AIBBggmNDoMsCvECnDG4CgYBaNgFIxcAABywmT0tOJnQQAAAABJRU5ErkJggg==","orcid":"","institution":"Marquette University","correspondingAuthor":true,"prefix":"","firstName":"Taiwo","middleName":"D","lastName":"Fasae","suffix":""},{"id":447977873,"identity":"795fb5f0-f8a2-4931-81c7-071d09d0ded3","order_by":1,"name":"Richard J Povinelli","email":"","orcid":"","institution":"Marquette University","correspondingAuthor":false,"prefix":"","firstName":"Richard","middleName":"J","lastName":"Povinelli","suffix":""}],"badges":[],"createdAt":"2025-02-04 18:38:17","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5960362/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5960362/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81605389,"identity":"c80f2b70-c897-4e13-898d-4b530c84081f","added_by":"auto","created_at":"2025-04-29 05:38:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1127790,"visible":true,"origin":"","legend":"","description":"","filename":"burnproboptimizationDME.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5960362/v1_covered_498a8013-90b5-48bf-a64e-146669cb6780.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Burn risk modelling and cost estimation for surface grinding optimization","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":"discover-mechanical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"discmecheng","sideBox":"Learn more about [Discover Mechanical Engineering](https://www.springer.com/journal/44245)","snPcode":"44245","submissionUrl":"https://submission.nature.com/new-submission/44245/3","title":"Discover Mechanical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"surface grinding, thermal damage, grinding burn, burn detection, optimization, probability","lastPublishedDoi":"10.21203/rs.3.rs-5960362/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5960362/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAchieving zero defects in surface grinding cost efficiency requires selecting the right parameters that minimize the likelihood of thermal damage, along with the understanding that parameter choices are reflective of these likelihoods. 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Grinding 2 mm downward cut depth on 85 mm x 203 mm x 11 mm Inconel 718 steel workpieces are performed. We demonstrate that the burn cost model inspires solutions that consider thermal damage risk quantitatively rather than qualitatively as done by the burn constraint model. 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