Computational Multi-objective Optimization of Friction Stir Welding Parameters for AZ31B Mg/6061-T6 Al Dissimilar Joints: A Meta-modeling Approach Based on Literature Data

Computational Multi-objective Optimization of Friction Stir Welding Parameters for AZ31B Mg/6061-T6 Al Dissimilar Joints: A Meta-modeling Approach Based on Literature Data | 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 Computational Multi-objective Optimization of Friction Stir Welding Parameters for AZ31B Mg/6061-T6 Al Dissimilar Joints: A Meta-modeling Approach Based on Literature Data Lotfi Beghdadi, Azeddine Beghdadi, Abdel Halim Zitouni, Mouloud Aissani, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8784393/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract This study proposes a computational framework for multi-objective optimization of friction stir welding (FSW) parameters in dissimilar AZ31B magnesium and 6061-T6 aluminum joints. Given the experimental complexity and cost associated with parameter optimization for dissimilar material systems, we developed predictive meta-models for peak temperature, residual stress, and distortion using response surface methodology calibrated against published experimental data. The temperature model demonstrated strong predictive capability (R² = 0.834, MAPE = 1.88%) when validated against twelve independent configurations from literature. A multi-objective genetic algorithm was subsequently employed to identify Pareto-optimal welding parameters that minimize thermal exposure, residual stresses, and geometric distortion. The analysis yielded 25 non-dominated solutions, with the compromise solution achieving 25.7% reduction in predicted residual stresses compared to baseline parameters while maintaining thermal stability. Optimal parameters were identified as: rotational speed 700 rpm, welding speed 61 mm/min, shoulder-to-pin ratio 2.51, and plunge force 6728 N. The results indicate that lower heat input conditions favor stress minimization in Mg-Al dissimilar joints, consistent with established thermo-mechanical principles. While experimental validation of the stress and distortion predictions remains necessary for absolute quantification, the methodology provides a cost-effective computational tool for preliminary process design and parameter screening, potentially reducing the experimental burden in industrial FSW development. Friction stir welding Multi-objective optimization Magnesium-aluminum dissimilar joints Genetic algorithm Residual stress Response surface methodology Computational process design Full Text Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2026 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Reviewers agreed at journal 23 Feb, 2026 Reviewers invited by journal 19 Feb, 2026 Editor assigned by journal 18 Feb, 2026 First submitted to journal 14 Feb, 2026 Editorial decision: Major Revisions Needed 10 Feb, 2026 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. 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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-8784393","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":593944513,"identity":"2839a8b9-0cf3-4e46-ad92-190b86af5751","order_by":0,"name":"Lotfi 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Given the experimental complexity and cost associated with parameter optimization for dissimilar material systems, we developed predictive meta-models for peak temperature, residual stress, and distortion using response surface methodology calibrated against published experimental data. The temperature model demonstrated strong predictive capability (R\u0026sup2; = 0.834, MAPE\u0026thinsp;=\u0026thinsp;1.88%) when validated against twelve independent configurations from literature. A multi-objective genetic algorithm was subsequently employed to identify Pareto-optimal welding parameters that minimize thermal exposure, residual stresses, and geometric distortion. The analysis yielded 25 non-dominated solutions, with the compromise solution achieving 25.7% reduction in predicted residual stresses compared to baseline parameters while maintaining thermal stability. Optimal parameters were identified as: rotational speed 700 rpm, welding speed 61 mm/min, shoulder-to-pin ratio 2.51, and plunge force 6728 N. The results indicate that lower heat input conditions favor stress minimization in Mg-Al dissimilar joints, consistent with established thermo-mechanical principles. 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