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.

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.

The paper presents the influence of simple structural modifications of the adherend on the fatigue properties of adhesive joints subjected to peel. The considered modifications consisted in making a chamfer and fillet on the front edge of the adherend. The purpose of such modifications was to locally increase the thickness of the adhesive layer in the area of stress concentration. Fatigue strength tests were carried out using an electrodynamic shaker at the resonant frequency of the flexible adherend. On the basis of fatigue strength tests carried out at the limited number of cycles equal to 2×106, it was shown that a local increase in the thickness of the adhesive layer in the front part of the joint allows a significant increase in the joint's fatigue lifetime and fatigue strength. The greatest effect was shown for the variant with the fillet R2. In this case, an increase in fatigue strength of 33.1% compared to the base variant was demonstrated. For the fatigue stress level of 20.25 MPa, an increase in fatigue lifetime of 337.7% was also demonstrated. Based on the conducted research, it was shown that the reason for the improvement of the fatigue properties of the joints due to the local increase in the thickness of the adhesive layer is the phenomenon of energy absorption in the frontal area of the joint. Absorption of energy that inhibits the process of fatigue results from, among others, local flexibility of the joint, as well as nucleation of cracks in the locally increased volume of the adhesive.

This study involved the optimization of the molded pieces manufacturing process from a poly-3-hydroxybutyrate-co-3-hydroxyvalerate biocomposite containing 30% wood flour by mass. The amount of wood flour and preliminary processing parameters were determined on the basis of preliminary tests. The aim of the optimization was to find the configuration of important parameters of the injection process to obtain molded pieces of good quality, in terms of aesthetics, dimensions, and mechanical properties. The products tested for quality were dog bone specimens. The biocomposite was produced using a single-screw extruder, whereas molded pieces were made using an injection molding process. The Taguchi method was applied to optimize the injection molding parameters, which determine the products quality. Control factors were selected at three levels. The L27 orthogonal plan was used. For each set of input parameters from this plan, four processing tests were performed. The sample weight, shrinkage, elongation at break, tensile strength, and Young’s modulus were selected to assess the quality of the molded parts. As a result of the research, the processing parameters of the tested biocomposite were determined, enabling the production of good-quality molded pieces. No common parameter configuration was found for different optimization criteria. Further research should focus on finding a different range of technological parameters. At the same time, it was found that the range of processing parameters of the produced biocomposite, especially processing temperature, made it possible to use it in the Wood Polymer Composites segment.