Experimental investigation, modeling and optimization of wire EDM process parameters for machining AA2024-B₄C self-lubricating composite

Abstract

Metal matrix composites (MMCs) are increasingly used across various manufacturing sectors, including automotive, defense, and aerospace, due to their exceptional strength-to-weight ratio, lightweight properties, high strength, and appreciable hardness when combined with suitable reinforcing materials. MMCs reinforced with carbide particles not only enhance the mechanical properties, but also exhibit self-lubricating characteristics, providing exceptional wear resistance. The self-lubricating properties of MMCs contribute significantly to minimizing the maintenance requirements, reducing operational costs, and advancing sustainability goals, rendering them indispensable for sectors such as aerospace, automotive, medical equipment, and energy. The present work addresses the challenges associated with machining advanced composite materials and proposes optimal machining parameters to overcome these difficulties. Here in the current investigation, aluminium alloy (AA2024) + 10 wt.% B₄C composite was selected as the workpiece material, and it was machined using a wire electric discharge machine. Response surface methodology was employed to develop predictive models for the output responses, namely surface roughness (Ra) and material removal rate (MRR). The accuracy of the predictive models was found to be 98.78% for Ra and 93.54% for MRR, demonstrating their reliability. To optimize the machining performance, both single-objective and multi-objective optimization approaches were used. Taguchi's signal-to-noise (S/N) ratio analysis was applied for single-objective optimization, while Pareto optimal fronts generated using the genetic algorithm facilitated the multi-objective optimization to maximize MRR and minimize Ra effectively.