Enhancing high-speed EDM performance of hybrid aluminium matrix composite by genetic algorithm integrated neural network optimization

• Published in: Journal of materials research and technology Vol. 31; pp. 4113 - 4127
• Main Authors: Ali, Muhammad Asad, Mufti, Nadeem Ahmad, Sana, Muhammad, Tlija, Mehdi, Farooq, Muhammad Umar, Haber, Rodolfo
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2024; Elsevier
• Subjects: Hybrid aluminium matrix composites; Material removal rate; Reinforcement particles; Stir-squeeze casting; Wire electric discharge machining; Wire wear ratio; Material removal rate; Reinforcement particles; Wire wear ratio; Hybrid aluminium matrix composites; Stir-squeeze casting; Wire electric discharge machining
• ISSN: 2238-7854; 2214-0697
• DOI: 10.1016/j.jmrt.2024.07.077

Hybrid aluminium matrix composites (HAMCs) are highly valued in manufacturing sectors but are difficult to machine conventionally due to reinforcements' inherent hardness and abrasiveness. This research finds high-speed wire electric discharge machining (WEDM) to be a potent solution for machining stir-squeeze-casted HAMC (AA2024 with ceramic nanoparticles: Al2O3, SiC, Si3N4, BN) and creating complex profiles with superior erosion characteristics. The erosion performance has been assessed in terms of material removal rate (MRR) for different profiles (plane, angular, and curve), and wire wear ratio (WWR) by employing machining variables, i.e., pulse voltage (PV), pulse current (PI), wire feed rate (WFR), pulse (P), and drum speed (DS). Results revealed that MRR_curve has highest MRR (37.84 mm3/min), followed by MRR_angular (36.07 mm3/min) and MRR_plane (34.40 mm3/min). The lowest WWR (0.0094%) was achieved at lower magnitudes of machining variables. The microscopic observations unveil shallow craters, minute-sized melt redeposits, and micro-pores on machined surface under the conditions of PV = 90 V, PI = 2 A, WFR = 13 m/min, P = 20 mu, and DS = 40 Hz. Optimization using non-dominated-sorting-genetic algorithm (NSGA-II) resulted in significant enhancements of 75.37, 73.90, and 76.01% in MRR_plane, MRR_angular, and MRR_curve, respectively, and a depreciation of 16.50% in WWR.