Multi-objective Optimization of Electrical Discharge Machining of Nimonic 75 using Teaching Learning Based Optimization (TLBO) Algorithm

Abstract

Superalloys have been widely used in aircraft, space and power plant industry because of their high performance, resistance to corrosion and oxidation at elevated temperature. Processing of superalloys by conventional machining process becomes difficult due to high tool wear and poor quality. In present paper, machining of Nimonic 75 (Ni-based Superalloy) is carried out using Electrical Discharge Machining (EDM) process. Experiments were carried out using Taguchi’s L18 (2^1×3^5) orthogonal array. The multi-objective optimization of performance measures namely material removal rate (MRR) and surface roughness (SR) have been carried out using Teaching Learning Based Optimization (TLBO) algorithm. Initially empirical models of MRR and SR were generated using Regression Method. The optimal parametric combination for copper tool electrode as peak current 6 amp, gap voltage 50 volt, pulse on time 200 µs, pulse off time 90 µs and lift time 2 sec and for brass tool electrode as peak current 12 amp, gap voltage 40 volt, pulse on time 120 µs, pulse off time 15 µs and lift time 2 sec are recommended according to TLBO algorithm. Confirmatory tests were conducted to validate the results obtained by TLBO method.