Evaluation the variability of Expert performance via Machine Learning(ML) Approach on Procedures prediction of Welding Experiments

Evaluation the variability of Expert performance via Machine Learning(ML) Approach on Procedures prediction of Welding Experiments | 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 Evaluation the variability of Expert performance via Machine Learning(ML) Approach on Procedures prediction of Welding Experiments Abobakr Alsufyani This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8030651/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 This study presents a comprehensive evaluation of the variance between expert-based estimation procedures and machine learning (ML)–driven optimization in predicting and improving the mechanical performance of welded joints. Experimental data were integrated with three supervised ML models—Random Forest, Gradient Boosting, and a Multilayer Perceptron (MLP) to quantify predictive accuracy, analyze variance, and optimize welding process parameters. The variance analysis demonstrated strong agreement between expert measurements and ML predictions, with mean deviations typically below 5%. Among the tested models, Random Forest achieved the most consistent accuracy across hardness, tensile strength, and impact energy. Optimization using the surrogate models identified the optimal parameter window as a welding current of 86–96 A, voltage of 9.5–10.5 V, and travel speed of 70–95 mm min⁻¹, corresponding to a heat-input range of approximately 0.50–0.70 kJ mm⁻¹. Comparative analysis confirmed that ML-optimized settings replicated expert estimations while reducing experimental trial-and-error and providing clear parametric insight into process behavior. The findings establish a reproducible framework for combining expert knowledge and ML techniques to enhance welding parameter design, improve mechanical consistency, and reduce manufacturing variability. Variance analysis Machine learning optimization Welding parameters Random Forest Gradient Boosting MLP Neural Network Heat input Mechanical properties Process optimization. Full Text Additional Declarations No competing interests reported. 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. 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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-8030651","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557100362,"identity":"53743186-bdbe-469e-8fc1-3f64576b29bc","order_by":0,"name":"Abobakr Alsufyani","email":"data:image/png;base64,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","orcid":"","institution":"King Saud University","correspondingAuthor":true,"prefix":"","firstName":"Abobakr","middleName":"","lastName":"Alsufyani","suffix":""}],"badges":[],"createdAt":"2025-11-04 15:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8030651/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8030651/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":97899323,"identity":"05302929-f5ce-4954-addf-89b4f07b62bf","added_by":"auto","created_at":"2025-12-10 15:43:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":964648,"visible":true,"origin":"","legend":"","description":"","filename":"11paper11Final.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8030651/v1_covered_776bc6c9-5c08-43c8-a1f3-cb6b1fe183ed.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation the variability of Expert performance via Machine Learning(ML) Approach on Procedures prediction of Welding Experiments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"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":"Variance analysis, Machine learning optimization, Welding parameters, Random Forest, Gradient Boosting, MLP Neural Network, Heat input, Mechanical properties, Process optimization.","lastPublishedDoi":"10.21203/rs.3.rs-8030651/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8030651/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study presents a comprehensive evaluation of the variance between expert-based estimation procedures and machine learning (ML)\u0026ndash;driven optimization in predicting and improving the mechanical performance of welded joints. Experimental data were integrated with three supervised ML models\u0026mdash;Random Forest, Gradient Boosting, and a Multilayer Perceptron (MLP) to quantify predictive accuracy, analyze variance, and optimize welding process parameters. The variance analysis demonstrated strong agreement between expert measurements and ML predictions, with mean deviations typically below 5%. Among the tested models, Random Forest achieved the most consistent accuracy across hardness, tensile strength, and impact energy. Optimization using the surrogate models identified the optimal parameter window as a welding current of 86\u0026ndash;96 A, voltage of 9.5\u0026ndash;10.5 V, and travel speed of 70\u0026ndash;95 mm min⁻\u0026sup1;, corresponding to a heat-input range of approximately 0.50\u0026ndash;0.70 kJ mm⁻\u0026sup1;. Comparative analysis confirmed that ML-optimized settings replicated expert estimations while reducing experimental trial-and-error and providing clear parametric insight into process behavior. 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