| Abstrakt: |
This article offers an experimental study focusing on the influence of input process parameters on the machinability of wire electrical discharge machining (WEDM) applied to metal matrix composites. Specifically, the study investigates hybrid-reinforced silicon carbide and graphite with base alloy aluminium (Al-6351). The composite work specimens were fabricated using squeeze casting and subjected to WEDM processing with a statistically controlled experimental design. Linear regression equations were derived for each response parameter using MINITAB 17 software to analyse the relationship between the response and process parameters. These equations were then optimized using the Elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) as a Multi-Objective Optimization Algorithm. The objectives of the optimization problem included material removal rate (MRR), surface roughness (SR), dimensional deviation (DD), and kerf. The optimization process yielded a set of Pareto optimal solutions, from which the single best compromise solution for the multi-objective optimization problem was determined. Subsequently, a confirmation test was done using the optimal process parameters obtained through the Genetic algorithm (GA). Remarkably, the optimized readings of MRR, SR, DD, and Kerf were closely matched with the corresponding experimental values from the confirmation test, indicating the successful identification of process parameters that led to favourable machinability outcomes for the composite material. Utilizing the compromise programming approach on the Pareto optimal solutions yielded a singular, optimal solution. The optimized values for the response parameters are MRR = 23.825 mm3/min, SR = 4.791 µm, DD = 3.173%, and Kerf = 0.268 mm. |
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