Abstract
This study investigated various performance measures - material removal rate (MRR), surface roughness (SR), and dimensional deviation (DD) - in wire cut electrical discharge machining of the Incoloy-800 superalloy under the effect of purposefully varied process inputs. Superalloys comprise a bright category of alloys that possess excellent mechanical strength at elevated temperatures, superior surface stability, magnificent creep resistance, and corrosion and oxidation resistance. Analysis of variance was performed to reveal influential and crucial inputs for machining measures; results were interpreted and modeled statistically. After conducting the experimental runs, the novel attempt has further been made to conduct the multi-response optimization with the desirability method and the teaching-learning-based optimization (TLBO) algorithm to develop the best process setting for optimizing all considered measures jointly. Furthermore, machined sample's microstructure analysis was conducted with the scanning electron microscopy (SEM) analysis. The op- timized parametric setting was obtained as follows: pulse on time: 2 mu s, pulse off time: 4 mu s, peak current: 2 A, and wire feed rate: 5 m/min. Obtained experimental values for MRR, SR, and DD were 0.936 mm/min, 3.797 mu m, and 0.084 mm, respectively, with the combined desirability value of 0.601. Optimization values of MRR, SR, and DD through the TLBO method varied from predicted values by 3.01%, 6.86%, and 6.32%, respectively.