Material Selection and Optimization of Green Drilling of Aluminium Alloys: MCDM-Pareto ANOVA and T-SRC

Abstract

Abstract The widespread use of aluminium alloys in the automotive, aerospace, and structural sectors necessitates the establishment of exploratory selection criteria to harness optimality at the various stages of product development. This study utilizes multi-criteria decision-making approaches to determine the most suitable aluminium alloy for thermal friction drilling. Additionally, the process optimization was done using the Taguchi super ranking concept (T-SRC), and the confirmation test using explicit dynamics simulation using ANSYS software. The ideal material selection was evaluated based on several factors, including yield strength, tensile strength, thermal conductivity, impact resistance, density, specific heat capacity, coefficient of thermal expansion, and percentage elongation. The best-ranked AA2024-T4 aluminium alloy underwent an integrated Taguchi Design of Experiments and optimization using Pareto ANOVA and subsequently ANSYS Explicit Dynamics process simulation. The MCDM Expert System derived from AHP, and ranking of materials using TOPSIS, EDAS, and VIKOR proved to be an effective material selection strategy from a set of alternatives for the thermal friction drilling process. By combining Multi-Criteria decision making (MCDM) with process simulation, material selection concerns in different industrial processes can be resolved, therefore eliminating the need for laborious tests.