The tensile testing of various materials to evaluate the influence of different machining parameters is a fundamental requirement in every industry. The objective of this study is to investigate the effects of temperature, the area of the contact point, and the operator on the tensile test of brass samples. This study employs a hybrid soft computing approach, integrating an adaptive network-based fuzzy inference system (ANFIS), genetic algorithm (GA) optimization, and design of experiments (DOE). By combining these techniques, the study aims to leverage their individual strengths and achieve superior results. The results reveal that the area of the contact point exerts the most significant influence on the tensile test, followed by the operator and temperature. The optimal levels of these parameters are determined to be a level of two for the operator, a level of three for the area of the contact point, and a level of one for the temperature. The study demonstrated that the hybrid soft computing method outperformed the traditional DOE method, achieving a substantial improvement in elongation of 32.9%. The optimized combination of machining parameters led to a notable enhancement in the brass samples’ tensile properties, highlighting the effectiveness of the applied methodology. The marginal error of only 0.72% in the hybrid approach showcases its high precision and reliability in determining the optimal levels of machining parameters. These findings underscore the potential of the Taguchi optimization method, ANFIS, and GA in achieving superior results in the tensile testing of materials, particularly in cases where multiple parameters are involved. The research results provide valuable insights for industries relying on precise material characterization, offering a robust methodology for optimizing tensile testing procedures. The study’s success in leveraging a hybrid soft computing approach serves as a promising avenue for future research in the field of material testing and optimization techniques.

This study systematically investigates and optimizes the influence of Fused Deposition Modeling (FDM) process parameters on the macroscopic tensile strength of polylactic acid (PLA) components. Addressing the need for predictable structural performance in 3D-printed parts, the novelty of this work lies in establishing a holistic, multi-stage optimization framework that maps the combined kinematic and geometric interactions of critical printing parameters directly to structural failure mechanisms and microstructural integrity. A systematic four-phase methodology was executed using a Taguchi L25 orthogonal experimental design to evaluate four manufacturing variables, namely layer thickness (P1), infill percentage (P2), nozzle retraction speed (P3), and nozzle travel speed (P4) across five discrete levels. Uniaxial destructive tensile testing on an ST Series Universal Testing System revealed that the initial ultimate tensile strength spanned a broad range from 20.04 MPa to a peak of 32.59 MPa. Signal-to-noise (S/N) ratio analysis (“Larger-the-Better”) and Analysis of Variance (ANOVA) at a 95% confidence level established layer thickness (P1) as the strictly dominant governing factor (F = 5.56), contributing 43.14% to total system variance. Parametric impact tracking confirmed secondary physical contributions from retraction speed (P3, 20.86%) and infill percentage (P2, 16.82%), while nozzle travel speed (P4, 3.90%) exhibited a negligible effect. Experimental confirmation testing using the identified optimal combination (0.20 mm layer thickness, 90% infill, 40 mm/s retraction speed, and 80 mm/s travel speed) yielded a peak ultimate tensile strength of 36.6 MPa, achieving a 12.3% improvement over the initial experimental maximum. Ultimately, this research offers a highly predictable, mathematically validated manufacturing strategy for converting empirical FDM adjustments into reliable and solid engineering components.