Optimization of Wire Electrical Discharge Machining Parameters for Inconel Alloys: A Comprehensive Review of Hybrid Techniques

Abstract

Superalloys, particularly nickel-based Inconel alloys, are extensively used in high-performance industries due to their superior strength, corrosion resistance, and thermal stability at elevated temperatures. However, their excellent mechanical properties also make them difficult to machine using conventional methods. Wire Electrical Discharge Machining (WEDM) has emerged as an effective non-traditional technique for precision machining of Inconel alloys, offering high accuracy and minimal residual stress. Numerous studies have employed optimization techniques such as Taguchi, Response Surface Methodology (RSM), Artificial Neural Networks (ANN), Genetic Algorithms (GA), and hybrid approaches to enhance machining efficiency. Among various parameters, pulse-on time and pulse-off time have been identified as the most significant factors influencing Material Removal Rate (MRR) and Surface Roughness (SR). Although several researchers have optimized these parameters individually, comprehensive multi-objective optimization addressing both MRR and SR simultaneously remains limited. The literature review and analysis highlight a significant research gap in integrating statistical and intelligent optimization methods for improved machinability of Inconel alloys. This study aims to explore hybrid optimization techniques to achieve superior machining performance and surface integrity, thereby enhancing the reliability and applicability of superalloy components in aerospace, nuclear, and energy sectors.