Multi-objective parametric optimization of nano powder mixed electrical discharge machining of AlSiCp using response surface methodology and particle swarm optimization

Abstract

Powder mixed electrical discharge machining (PMEDM) has gained popularity in the current era owing to its benefits of providing better material removal rate (MRR), less electrode wear rate (EWR) and improvement in surface finish. The use of powders enhances the machining characteristics of the EDM processes. Low voltage current (LVC), high voltage current (HVC), pulse-on time (Ton), pulse-off time (Toff) and flushing pressure (FP) are the input variables on which certain machining parameters such as material removal rate (MRR), surface roughness (Ra) and tool wear rate (TWR) are analysed. A copper electrode of 99.98% purity with a diameter of 12mm was used to cut AlSiCp12% metal matrix composite (MMC) in EDM. Box Behnken design was used for planning the experimental run. The parameters were optimized using desirability approach as multi-objective optimization technique for predicting the significance of the parameters. In addition to all this, particle swarm optimization (PSO) was implemented for predicting the results and hence error analysis was done for the set of experiments. Moreover, a confirmatory test was carried out with the parametric settings obtained from PSO and hence the error percentage was determined. Validation tests showed close relationship of predicted and experimental results.