Enhancing machining efficiency in hybrid metal matrix composites through EDM parameter optimization via grey relational analysis

Abstract

In summary, this study utilized stir casting to develop three distinct aluminium hybrid composites incorporating boron carbide and alumina. These composites were identified as Al7075 + 12%B4C + 6%Al2O3, Al7075 + 6%B4C + 6%Al2O3, and Al7075 + 6%B4C + 12%Al2O3. The novelty of this research lies in the unique combination of materials and their proportions, offering enhanced properties yet unexplored in this specific configuration. The research focused on machining these composite materials using EDM and employed a Taguchi L27 orthogonal array design to plan the machining tests. Key process parameters considered were gap voltage (V), duty cycle (%), current (A), and different percentages of reinforcement content. This aspect adds a significant novel dimension to the study, as the impact of EDM machining parameters on hybrid composites with this particular composition has not been extensively explored previously. The study applied Grey Relational Analysis to evaluate the impact of EDM machining process parameters on critical output responses, specifically material removal rate (MRR) and tool wear rate (TWR). SEM images revealed redeposited molten material, craters, and surface cracks, emphasizing the importance of controlling discharge energy and current levels for desired surface quality. An optimal configuration was identified peak current 2 A, pulse duration 100 μs, duty factor 20%, Composition 2 for improved surface attributes. These qualitative and quantitative insights inform process optimization strategies for EDM machining of aluminium hybrid composites.