Optimization design for die-sinking EDM process parameters employing effective intelligent method

Abstract

AbstractElectrical discharge machining (EDM) is a highly regarded method for producing ultra-precise mechanical parts. In this study, the process parameters of die-sinking EDM using copper electrodes and American Iron and Steel Institute (AISI) P20 tool steel workpieces are optimized for various output responses. The study surveys three input parameters, including Current (I), Pulse on Time (Ton), and Pulse Off Time (Toff). Some statistical methods, such as Taguchi and Analysis of Variance (ANOVA), are applied to find the optimal set of parameters for the output responses, consisting of Material Removal Rate (MRR), Electrode Wear Rate (EWR), and Surface Roughness (SR), and determine the most influential input factor. With the L9 Orthogonal Array (OA), the analytical results demonstrate the optimal parameter set for MRR is I = 6 A, Ton = 120 µs, and Toff = 30 µs, while those optimal values for EWR and SR are I = 2 A, Ton = 120 µs, and Toff = 90 µs and I = 2 A, Ton = 60 µs, and Toff = 30 µs, respectively. The study also indicates that input factor I has the most effect on the output responses, followed by Ton and Toff. Moreover, Grey relational analysis in the Taguchi method is also employed for multi-response optimization. The optimal parameter set for the three output factors is I = 6 A, Ton = 120 µs, and Toff = 60 µs, respectively. In this research, the microstructure and recast layer of the machined surfaces are investigated using optical microscopy as well.

Keywords

• material removal rate
• electrode wear rate
• surface roughness
• Taguchi
• ANOVA analysis
• optimization design