Multi-Response Optimization of Electrical Discharge Machining of 17-4 PH SS Using Taguchi-Based Grey Relational Analysis

Abstract

Multiple response optimization of the machining of 17-4 PH stainless steel material, which is difficult to process with traditional methods, with EDM was made by Taguchi-based grey relational analysis method. Surface roughness (Ra), material removal rate (MRR), and electrode wear rate (EWR) were the responses, while current, pulse-on time, pulse-off time, and voltage were chosen as process parameters. According to the multi-response optimization, the experiment level that gave the best result was A1B2C2D2. Optimum machining outputs were found as A1B3C1D1 using the Taguchi method. As a result of the Taguchi analysis and ANOVA, it was determined that the significant parameters according to multiple performance characteristics were current (56.22%) and voltage (22.40%). The surfaces of the best GRG and optimal sample were examined with XRD, SEM and EDX analysis and the effects on the surfaces were compared.

Keywords

• edm
• 17-4 ph ss
• taguchi-based grey relational analysis
• anova
• surface characterization