Machine learning and exploratory data analysis for predicting tensile and thermal responses in friction stir spot welding

Abstract

Friction stir spot welding (FSSW) has gained increasing attention over the last decade due to its promising performance compared to conventional joining methods for similar metals. However, the thermal and tensile responses in this process are highly nonlinear. This study aims to explore the thermal and tensile performance of aluminum joints welded by FSSW using an innovative method based on exploratory data analysis (EDA), followed by several machine learning (ML) approaches. The welding parameters investigated in this study were tool rotational speed, dwelling time, and aluminum sheet thickness. The ML methods included linear and nonlinear regression models for welded joints at different welding parameters. We evaluated Bayesian ridge, elastic-net, support vector regression (SVR), random forest, polynomial regression (nonlinear), and robust regression. The random forest algorithm provided accurate predictions for lap-shear fracture load (R² = 0.96, mean squared error [MSE] = 0.01, and mean absolute error [MAE] = 0.07) in tensile performance, whereas the elastic net performed worst. Model-to-model differences were smaller for thermal performance, with the random forest model yielding the most accurate predictions (R² = 0.97, MSE = 26.51, and MAE = 3.86) while the SVR yielded the least accurate predictions. The study indicated that using EDA to address anomalies in welding conditions provides valuable insights into the best ML methods for predicting the thermal and mechanical performance of welding joints.

Keywords

Friction stir spot welding / Machine learning / Exploratory data analysis / Tensile performance / Thermal performance

Acknowledgments

None.

Funding

None.

Conflict of interest

The authors declare that they have no competing interests.

Author contributions

Conceptualization: Raheem Al-Sabur

Formal analysis: Sajad N. Alasadi

Investigation: All authors

Methodology: Sajad N. Alasadi

Writing-original draft: Sajad N. Alasadi

Writing-review & editing: Raheem Al-Sabur

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

The data presented in this study are available upon request from the corresponding author.

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