Stochastic Optimization of Surface Roughness Using Monte Carlo Algorithms

Stochastic Optimization of Surface Roughness Using Monte Carlo Algorithms | 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 Stochastic Optimization of Surface Roughness Using Monte Carlo Algorithms Elvir Čajić This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4466876/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In this paper, we investigate the application of Monte Carlo algorithms for the optimization of surface roughness in production processes. Using stochastic methods, a mathematical model was developed that accurately predicts surface roughness based on key processing parameters. Simulations were performed on samples of different materials, where the effects of changes in input parameters on the final roughness were analyzed. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling a better control of the quality of the final processing. Algorithms are implemented using MATLAB and Python, which enables flexibility and efficiency in data analysis. The results show that Monte Carlo algorithms can significantly improve the accuracy of process prediction and optimization, enabling better control of the quality of finishing. In addition, this approach reduces the need for an experimental approach, resulting in reduced costs and processing time. Applied Mathematics Computational Mathematics stochastic optimization Monte Carlo algorithms surface roughness mathematical modeling Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-4466876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":305941623,"identity":"3ad79245-0ef9-4def-aefa-77481ab9933d","order_by":0,"name":"Elvir Čajić","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYJACgwQGGzl+ECuhgHgtacaSDSAtBsRbdDjR4ABYNxFq+dkPPyh4UJOWYHx+deKHBwYM8vxiB/BrkexJMzBIOGaTZ3bj7WYJoMMMZ85OwK/F4AaDgUFiQ1qx2Y2zG0BaEgxuE9Bif4P9A1DL4cTNM85u/kGUFgMJHgOwlg38vduIs0XiTE4B0C9pxhI3eLdZJBhIEPYLf/vxbYY/aoBR2X92880fFTby/NIEtAABGyQyJMAqJQgqBwHmBxD7DhClehSMglEwCkYgAAAhOkWfVRVKGQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-4967-6269","institution":"European University Kallos Tuzla","correspondingAuthor":true,"prefix":"","firstName":"Elvir","middleName":"","lastName":"Čajić","suffix":""}],"badges":[],"createdAt":"2024-05-23 12:33:47","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4466876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4466876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57059191,"identity":"5dcbe09a-c77a-4116-8c30-a8abfad553de","added_by":"auto","created_at":"2024-05-24 05:35:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":579154,"visible":true,"origin":"","legend":"","description":"","filename":"StochasticoptimizationofsurfaceroughnessSlobodanNicin23.5.24.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4466876/v1_covered_70e3d624-97c7-4286-9586-3fb5df6a1bca.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eStochastic Optimization of Surface Roughness Using Monte Carlo Algorithms\u003c/strong\u003e\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Central European University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"stochastic optimization, Monte Carlo algorithms, surface roughness, mathematical modeling","lastPublishedDoi":"10.21203/rs.3.rs-4466876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4466876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, we investigate the application of Monte Carlo algorithms for the optimization of surface roughness in production processes. 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