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.

This study presents the optimization of the friction stir welding (FSW) process using polynomial regression to predict the maximum tensile load (MTL) of welded joints. The experimental design included varying spindle speeds from 600 to 2200 rpm and welding speeds from 100 to 350 mm/min over 28 experimental points. The resulting MTL values ranged from 1912 to 15,336 N. A fifth-degree polynomial regression model was developed to fit the experimental data. Diagnostic tests, including the Shapiro–Wilk test and kurtosis analysis, indicated a non-normal distribution of the MTL data. Model validation showed that fifth-degree polynomial regression provided a robust fit with high fitted and predicted R² values, indicating strong predictive power. Hill-climbing optimization was used to fine-tune the welding parameters, identifying an optimal spindle speed of 1100 rpm and a welding speed of 332 mm/min, which was predicted to achieve an MTL of 16,852 N. Response surface analysis confirmed the effectiveness of the identified parameters and demonstrated their significant influence on the MTL. These results suggest that the applied polynomial regression model and optimization approach are effective tools for improving the performance and reliability of the FSW process.

Multi-objective process optimization is critical in intelligent manufacturing, especially where complex, nonlinear interactions among parameters can significantly affect product quality. This study demonstrates a data-driven approach to optimizing Refill Friction Stir Spot Welding (RFSSW) parameters for an AA2024-T3 aluminum alloy. First, a 3³ full-factorial design of experiments was conducted to generate training and validation data on three pivotal process variables: rotational speed, plunge depth, and welding time. Statistical analysis using ANOVA highlighted plunge depth as the most influential factor, alongside notable interaction effects among the parameters. To build predictive models of joint load capacity, six machine learning techniques (MLP, RBF, GPR, k-NN, SVR, and XGBoost) were evaluated via cross-validation. XGBoost delivered the most accurate predictions, reaching R² values up to 0.89 with the lowest MAE and RMSE. Model interpretation methods such as feature importance and SHAP confirmed the dominant role of plunge depth, as suggested by ANOVA. The crux of the study lies in a multi-objective optimization framework using the NSGA-II evolutionary algorithm, targeting maximum weld strength in two distinct shear-testing variants (pure shear and free shear). The procedure generated a Pareto frontier of optimal parameter sets, from which a maximin strategy selected a high-performing compromise solution. These findings underscore the value of combining statistical methods, advanced machine learning, and evolutionary optimization in refining solid-state joining processes. More broadly, this integrated methodology provides a robust template for intelligent manufacturing applications that require balancing multiple performance objectives under complex process conditions.

This study investigates the influence of polymer interlayers on the mechanical properties and fracture behavior of friction stir welded EN AW-2024-T3 aluminum alloy joints. Aqualock AL 6002 (Adhesive_1) and 3M Adhesion Promoter 86A (Adhesive_2) were selected as polymeric sealants for EN AW-2024-T3 aluminum alloy interfaces. Force analysis revealed that the choice of polymer interlayer significantly affects the axial force during welding, with Adhesive_1 joints showing a significant reduction in force compared to other variants. The axial force during FSW in Adhesive_1 was 18% less than in Adhesive_2. Cross-sectional analysis revealed distinct features in joint morphology and defects, with Adhesive_1 joints exhibiting favorable thermal stability and minimal defects compared to Adhesive_2 joints. Tensile strength analysis showed a significant increase in load capacity for the Adhesive_1 (9470N) joint, while the Adhesive_2 (5030N) joint exhibited reduced strength due to inadequate heat flow. The Adhesive_1 joint showed a 93% increase in tensile strength compared to Adhesive_2. The mixing of Adhesive_2 with the joint area produced hard complex particles that reduced the strength of the final joint. Fracture analysis revealed complex fracture mechanisms, with Adhesive_1 joints exhibiting ductile fracture zones and Adhesive_2 joints exhibiting quasi-cleavable intergranular cracking. Microhardness distribution analysis showed variation between the joint variants, with Adhesive_2 joints showing higher microhardness in the weld nugget.

In this study a new ceramics tools with different groove distributions were designed and manufactured in order to enrich technological storage of joining thin-wall structures and obtain sound joint with high quality of alclad AA2024-T3 alloy of 0.5 mm in thickness. Four types of tools were tested, without grooves, with 1, 2 and 6 grooves. The tools are made of two materials. The straight shank is made from tungsten carbide and tool body made from ceramics strengthened with whiskers. The influence of technological parameters on the strength of FSW joints was tested by the RSM (Response Surface Methodology) and ANOVA (analysis of variance) method. The least durable weld is produced by a tool without grooves. The 1 and 2-flute tool produces a good quality weld over a wide range of tool speeds. It has been shown that the grooves on the tool shoulder significantly affect the quality of the obtained FSW joint.