## 4 Discussion  
The mechanical testing results obtained through the DOE analysis (Section 3.1) start from the essence of the Taguchi DOE, which is the ability to construct an efficient and informative experimental design. By selecting fewer factors and levels, we aimed to simplify the experimental process and focus on the most influential variables while maintaining statistical robustness (as the ANOVA exhibited in Section 3.2). This approach allows us to achieve reliable results with fewer experiments than traditional full factorial designs, making the Taguchi DOE a valuable tool for experimental optimization. This approach is inherent to the Taguchi DOE's capabilities and aligns with the principles of sound experimental design. To analyze the results of evaluating the selected parameters on the ultimate tensile strength, we analyzed the hardness (Section 3.3) and the microstructure (Section 3.4); also, we conducted a hardening phase study (Section 3.5) and performed a fracture evaluation (Section 3.6). Yield strength is relevant in mechanical testing evaluation, especially when the material's response to plastic deformation is predominant. However, we focus on UTS from the specific nature of processes such as welding and similar procedures, where the tensile strength often determines the final response due to several factors, including the variability in the 0.2% yield strength resulting from the mixture of microstructures generated during the cooling process. This variability can significantly impact the material's performance, particularly its ability to withstand applied stresses and resist deformation. In welding, the capacity of joints to withstand stress emphasizes the importance of considering the ultimate tensile strength in evaluating weldability and joint integrity [27]. Furthermore, in applications such as wear-resistance assessments, where hardness measurements serve as an initial indicator, ultimate tensile strength plays a crucial role, as it is directly correlated with hardness. Similarly, in phase transformation processes, the material's tensile strength is a key indicator of its response to heat-treatment, which is a critical consideration in various industries. The Taguchi design of experiment is a potent and efficient methodology distinctively adept at discerning crucial factors, minimizing variability, enhancing performance, and conserving resources [18]. Particularly in assessing mechanical properties such as tensile strength, its application transcends traditional testing and measurement approaches (often performed as a trial-and-error procedure), offering a proficient and cost-effective means of optimization. Moreover, this methodology is evaluated using ANOVA to propose a prediction model that explains the contribution of each variable in the heat-treatment process to the final UTS value. After ANOVA was applied to the Taguchi DOE, we obtained a regression model where the ultimate tensile strength of the alloy was linearly related to four factors: solutionizing temperature, aging temperature, solutionizing time, and aging time. It is important to note that the variable \"aging temperature\" was the least influential factor according to the analysis of variance, referring specifically to its relationship with the model and its generalization capability. This does not imply that aging temperature does not influence the outcome of the heat-treatment process because aging temperature is crucial in determining the final tensile strength. Comparing the determination coefficient, the regression model obtained \(R^{2}=93.18\%\). Thus, the model accounts for about 93% of the variability in the tensile strength response. Note that for the multiple regression model for the tensile strength data, \(R^{2}_{adj}=91.95\%\). Therefore, we would conclude that adding the variable of aging temperature to the model does result in a meaningful reduction in unexplained variability in the response. A cross-validation approach was employed to assess how well the model can predict results in different datasets.