Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches
- Title
- Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches
- Creator
- Keshavamurthy, R.; Naveena, B.E.; Gowda, P. N. Vikram; Shrivathsa, T.V.
- Description
- 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.
- Source
- Journal of Materials Engineering and Performance;Volume;35;Issue;12;pp.11533-11551
- Date
- 01-01-2026
- Publisher
- Springer
- Subject
- aluminum alloys; deep learning; friction stir spot welding; machine learning; neural network; predictive modeling; regression
- Coverage
- 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
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 10599495; CODEN: JMEPE
- Format
- online
- Language
- English
- Type
- Article
Collection
Citation
Keshavamurthy, R.; Naveena, B.E.; Gowda, P. N. Vikram; Shrivathsa, T.V., “Performance Evaluation of Friction Stir Spot Welding of Al 5754 and Al 6111 using Machine Learning Approaches,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/21979.