A Comprehensive Experimental and Multi-Criteria Decision-Making Framework for Optimizing SS316/Ti6Al4V Powder Metallurgy Composites

Resumo

The development of austenitic stainless steel-based matrix composites with powder metallurgy were attracting interest for many uses in the aerospace and defense sectors. The influence of sintering temperature (1100, 1200, and 1300 °C), compaction pressure (550, 650, and 750 MPa), Ti6Al4V content (5, 7, and 9wt%), and sintering duration (45, 90, and 135 minutes) on the density, hardness and compressive strength of SS316/Ti6Al4V composites was evaluated utilizing Taguchi, ANOVA, and response surface methodologies. The research employed L9 OA based on Taguchi method. The composite powder were compressed using single-action die subsequently sintered at muffle furnace. The Simultaneous Evaluation of Criteria and Alternatives (SECA) approach was employed to obtain the significance of criteria for performance assessment. According to SECA analysis, the density of 0.1592, the hardness of 0.1363, and the compressive strength of 0.1347 was obtained. The application of MARCOS for ranking evaluation indicates that composite reinforced with Ti6Al4V demonstrated the most advantageous physical and mechanical properties. The verification of ranking through several decision-making confirms the efficacy and reliability of suggested method. The S/N ratio-based Taguchi approach identified the optimal input parameters, whereas ANOVA evaluated the individual contribution percentage of all factors. The response surface methodology was employed to assess contour plots, surface plots and regression equations for response qualities. The ANOVA results indicated that compaction pressure significantly influenced the properties of the fabricated composites, followed by the percentage of Ti6Al4V, sintering time, and sintering temperature, in that order. The results of the SECA-MARCOS approach demonstrate that the formulation with 7 wt.% Ti6Al4V is the most advantageous for defense and aerospace applications. Consequently, the formulated models of response qualities for costly trial tests, thereby enhancing the diminishing production costs and efficiency of the optimization process.