Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Keshavamurthy, R.; Naveena, B.E.; Gowda, P. N. Vikram; Shrivathsa, T.V. Title A name given to the resource Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Journal of Materials Engineering and Performance;Volume;35;Issue;12;pp.11533-11551 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/s11665-025-11766-6" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/s11665-025-11766-6</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105013271257?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105013271257?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Keshavamurthy R., Department of Mechanical and Automobile Engineering, CHRIST University, Karnataka, Bengaluru, 560074, India; Naveena B.E., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560078, India; Gowda P.N.V., Department of Automobile Engineering, Dayananda Sagar College of Engineering, Bangalore, 560078, India; Shrivathsa T.V., Department of Artificial Intelligence and Machine Learning, Shri Madhwa Vadiraja Institute of Technology and Management, Karnataka, Bantakal, 574115, India Description An account of the resource This study evaluates advanced machine learning (ML) and deep learning (DL) models for predicting the tensile shear and bending strength of friction stir spot welding joints involving Al 5754 and Al 6111 alloys. ML techniques include Linear Regression, Decision Tree, Random Forest (RF), K-Nearest Neighbors, Support Vector Regression, and XGBoost, while DL models comprise Recurrent Neural Network (RNN) and Backpropagation Neural Network (BPNN). The models were assessed for discrepancies between experimental and predicted results, with the best-performing model identified using R-squared (R2), Root-Mean-Square Error, Mean Square Error, and Mean Absolute Error. The data preprocessing phase included feature scaling and an 85:15 train-test split. Key input process parameters included spindle speed, dwell time, plunge depth, and tool pin profile. The results demonstrate that XGBoost yielded the highest predictive accuracy, achieving an R2 score of 99.99% for both tensile shear and bending strength, while RF offered a strong balance between accuracy and robustness. Other ML models struggled with the datasets complexity, resulting in lower performance. Among DL approaches, the BPNN outperformed the RNN, achieving approximately 99.8% accuracy by effectively capturing complex data patterns. ASM International 2025. Subject The topic of the resource aluminum alloys; deep learning; friction stir spot welding; machine learning; neural network; predictive modeling; regression Publisher An entity responsible for making the resource available Springer Relation A related resource ISSN: 10599495; CODEN: JMEPE Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online