ArticleMaterials (Basel, Switzerland)2024
Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques.
Article in Materials (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Physics-Guided Machine Learning for Performance Prediction and Multi-Objective Optimization of High-Conductivity Aluminum Conductors.Materials (Basel, Switzerland) · 2026Article
- Investigation of the Phosphorus Effect on Solidification Cracking in Cu-Steel Single-Mode Fiber-Laser Welds for Reliable Li-Ion Battery Busbar Assembly.Materials (Basel, Switzerland) · 2025Article
- Integrated multiobjective optimization of RFSSW parameters for AA2024-T3 using ANOVA machine learning and NSGA II.Scientific reports · 2025Article
- FSW Optimization: Prediction Using Polynomial Regression and Optimization with Hill-Climbing Method.Materials (Basel, Switzerland) · 2025Article
- Increasing Exploitation Durability of Two-Layer Cast Mill Rolls and Assessment of the Applicability of the XGBoost Machine Learning Method to Manage Their Quality.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors.
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Abstract
This study optimized friction stir welding (FSW) parameters for 1.6 mm thick 2024T3 aluminum alloy sheets. A 3 × 3 factorial design was employed to explore tool rotation speeds (1100 to 1300 rpm) and welding speeds (140 to 180 mm/min). Static tensile tests revealed the joints' maximum strength at 87% relative to the base material. Hyperparameter optimization was conducted for machine learning (ML) models, including random forest and XGBoost, and multilayer perceptron artificial neural network (MLP-ANN) models, using grid search. Welding parameter optimization and extrapolation were then carried out, with final strength predictions analyzed using response surface methodology (RSM). The ML models achieved over 98% accuracy in parameter regression, demonstrating significant effectiveness in FSW process enhancement. Experimentally validated, optimized parameters resulted in an FSW joint efficiency of 93% relative to the base material. This outcome highlights the critical role of advanced analytical techniques in improving welding quality and efficiency.
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