{"paper_id":"0e81cf90-7cad-459e-9f4a-c4fad1cf4b4d","body_text":"Effect of Welding Parameters and Artificial Intelligence-Based Prediction of Maximum Temperature in Friction Stir Welding of AA3003 Alloy | 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 Effect of Welding Parameters and Artificial Intelligence-Based Prediction of Maximum Temperature in Friction Stir Welding of AA3003 Alloy Amina Belaribi, Ismail Chekalil, Abdelkader Miloudi, Réda Adjoudj, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7123190/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 06 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted 5 You are reading this latest preprint version Abstract The present study highlights the influence of feed rate, rotational speed, and tool inclination angle on the evolution of the maximum temperature generated during friction stir welding (FSW) of aluminum AA3003. To predict these thermal variations, six machine learning models were developed and trained using a dataset composed of 64 experimental trials covering a wide range of process parameters. The models include three artificial neural networks (ANNs) optimized using the Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) algorithms; one support vector machine (SVM) with a quadratic kernel; and two Gaussian Process Regression (GPR) models with Matérn 5/2 and exponential kernels. The models were evaluated using standard statistical indicators (RMSE, MAE, R²). The results demonstrate the superiority of the GPR model with a Matérn 5/2 kernel, with an RMSE of less than 0.02°C and an R² coefficient of determination close to unity. This model also stood out for its robustness on unprecedented configurations, with a relative error of less than 1.6%. The proposed approach demonstrates the potential of machine learning techniques to model the complex thermal phenomena of FSW accurately and represents a step towards intelligent predictive control of welding processes. FSW temperature prediction LM BR GPR Full Text Cite Share Download PDF Status: Published Journal Publication published 06 Nov, 2025 Read the published version in The International Journal of Advanced Manufacturing Technology → Version 1 posted Editorial decision: Major Revisions Needed 18 Sep, 2025 Reviewers agreed at journal 17 Jul, 2025 Reviewers invited by journal 17 Jul, 2025 Editor assigned by journal 16 Jul, 2025 First submitted to journal 14 Jul, 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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To predict these thermal variations, six machine learning models were developed and trained using a dataset composed of 64 experimental trials covering a wide range of process parameters. The models include three artificial neural networks (ANNs) optimized using the Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) algorithms; one support vector machine (SVM) with a quadratic kernel; and two Gaussian Process Regression (GPR) models with Mat\\u0026eacute;rn 5/2 and exponential kernels. The models were evaluated using standard statistical indicators (RMSE, MAE, R\\u0026sup2;). The results demonstrate the superiority of the GPR model with a Mat\\u0026eacute;rn 5/2 kernel, with an RMSE of less than 0.02\\u0026deg;C and an R\\u0026sup2; coefficient of determination close to unity. This model also stood out for its robustness on unprecedented configurations, with a relative error of less than 1.6%. 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