Forecasting the residual stress components in wires using an artificial neural network

Forecasting the residual stress components in wires using an artificial neural network | 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 Forecasting the residual stress components in wires using an artificial neural network Dmitriy Demin, Ilya Grebenkin, Alexey Barinov This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6787728/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 4 You are reading this latest preprint version Abstract In this paper, a computer simulation of round wire drawing processes with different equations of state for steel A12 has been carried out. In addition, the methods of improving the configuration of neural networks based on multilayer perceptron (MLP) for estimating the distributions of residual stress tensor components have been investigated. The study demonstrated that the strain rate exerted a significant influence on the character of the processes, particularly within the central region (0r–0.4r) of the investigated specimens. In addition, the employment of software tools for the purpose of tuning the hyperparameters of trained machine learning models, including Optuna, BayesianOpt, and Skopt, has been demonstrated to enhance the predictive capability of the models. Consequently, this results in an improvement in the accuracy of the obtained distributions of the required characteristics. FEM wire drawing residual stresses MLP Optuna BayesianOpt Skopt Full Text Supplementary Files usermanual.pdf Cite Share Download PDF Status: Published Journal Publication published 21 Oct, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviewers agreed at journal 04 Jun, 2025 Reviewers invited by journal 04 Jun, 2025 Editor assigned by journal 03 Jun, 2025 First submitted to journal 30 May, 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-6787728","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466625041,"identity":"5c11655e-06db-48bc-9361-40c516d8db00","order_by":0,"name":"Dmitriy Demin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAn0lEQVRIiWNgGAWjYDCCAwkMDAkMNgkka0kjVQsDw2EStPAdTz664eGO83ny7Q1sn3mI0SJ55lnajcQzt4sNzhxgnk2UFoMbOWY3EttuJ26QSGBmJlJL/jeglnOJ8+c/IFpLDhtQy4HEhhsMRGoB+gXksOTEDWcSmxnnEKMFGGLPbv5ss0uc3374MMMbYrQgAcYGEjWMglEwCkbBKMAJAOMhOhtFrdVaAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1369-1130","institution":"HSE Tikhonov Moscow Institute of Electronics and Mathematics: Nacional'nyj issledovatel'skij universitet Vyssaa skola ekonomiki Moskovskij institut elektroniki i matematiki imeni A N Tihonova","correspondingAuthor":true,"prefix":"","firstName":"Dmitriy","middleName":"","lastName":"Demin","suffix":""},{"id":466625042,"identity":"a3a5b287-f8e2-4cc8-a55c-05d756ffee13","order_by":1,"name":"Ilya Grebenkin","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ilya","middleName":"","lastName":"Grebenkin","suffix":""},{"id":466625043,"identity":"4fbb27a5-ed1a-4d5a-a081-9a4f4f2d0f98","order_by":2,"name":"Alexey Barinov","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Alexey","middleName":"","lastName":"Barinov","suffix":""}],"badges":[],"createdAt":"2025-05-30 22:31:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6787728/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6787728/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00170-025-16757-4","type":"published","date":"2025-10-21T16:16:38+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94490322,"identity":"dd88910e-93c5-43ae-b560-a5b965aae1e6","added_by":"auto","created_at":"2025-10-27 17:09:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":22691149,"visible":true,"origin":"","legend":"","description":"","filename":"Forecastingtheresidualstresscomponents.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787728/v1_covered_97aff9f4-5f01-4763-a2c6-d5957ce8dc8b.pdf"},{"id":84098623,"identity":"abdf8300-21fa-4b0e-82fc-696a267104f4","added_by":"auto","created_at":"2025-06-06 18:23:11","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":418495,"visible":true,"origin":"","legend":"","description":"","filename":"usermanual.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6787728/v1/360f1b8da5b2d013d4dd8728.pdf"}],"financialInterests":"","formattedTitle":"Forecasting the residual stress components in wires using an artificial neural network","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":"FEM, wire drawing, residual stresses, MLP, Optuna, BayesianOpt, Skopt","lastPublishedDoi":"10.21203/rs.3.rs-6787728/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6787728/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, a computer simulation of round wire drawing processes with different equations of state for steel A12 has been carried out. 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