Optimizing WEDM Parameters Using Swarm Intelligence: A Multi-Objective Approach to Improve Machinability and Cost-Efficiency

Abstract

This study aims to optimize the Wire-Cut Electrical Discharge Machining (WEDM) parameters for Inconel 800, a high-performance superalloy known for its remarkable mechanical properties and resistance to elevated temperatures. The research leverages Particle Swarm Optimization (PSO) to improve machining outcomes, including material removal rate, surface finish, and cost-efficiency. A structured experimental approach, following Taguchi's L₁₈ design, was used to evaluate the effects of key machining parameters such as pulse on-time, pulse off-time, peak current, and spark gap voltage. The results demonstrate that the PSO model significantly enhances machining performance by reducing surface roughness and increasing material removal rate (MRR), showcasing marked improvements in efficiency. With a mean prediction error of < 1%, the PSO model proves highly accurate and reliable. Additionally, the study examines the economic aspects of WEDM by calculating the total machining costs, which include power, wire, and dielectric fluid consumption. By filling a critical research gap in the machining of Inconel 800, this work offers valuable insights into optimizing WEDM processes for superalloys. The findings highlight the potential of PSO as a powerful tool for multi-objective optimization in advanced manufacturing applications.