Open AccessOptimization of process parameters in machining of nimonic super-alloy on EDM using genetic algorithm
Author(s) -
Madderla Sandhya,
D. Ramasamy,
Irshad Ahamad Khilji,
Anil Kumar,
S. Chandramouli,
Gulshan Kumar
Publication year - 2020
Publication title -
maejo international journal of energy and environmental communication (mijeec)
Language(s) - English
Resource type - Journals
ISSN - 2774-0064
DOI - 10.54279/mijeec.v2i1.244951
Subject(s) - nimonic , taguchi methods , superalloy , machining , electrical discharge machining , materials science , surface roughness , duty cycle , orthogonal array , mechanical engineering , design of experiments , voltage , metallurgy , engineering , alloy , composite material , mathematics , statistics , electrical engineering
This project aims to investigate and predict the optimal choice for each EDM parameter using Taguchi Method by conducting a limited number of experiments on “Nimonic” Material. These parameters have a significant influence on the machining characteristics like MRR and TWR. Taguchi design of experiments (DOE) are implemented, particularly L9 orthogonal array is chosen and the effect of dominating process parameters is evaluated using analysis of variance. Nimonic refers to a family of Nickel-based high-temperature low creep superalloys. Due to its ability to withstand very high temperatures, Nimonic is ideal for typical applications such as aircraft parts, gas turbine components and blades, exhaust nozzles etc., for instance, where the pressure and heat are extreme. However, the conventional methods are not suitable to machine the hardest material such as Nimonic superalloy. The EDM, one of the popular unconventional machining methods, is used to the machine with a copper electrode, which in turn uses Taguchi methodology to analyze the effect of each parameter on the machining characteristics. The optimal choice for each EDM parameter such as peak current, gap voltage, duty cycle and pulse on time using the Taguchi method and Genetic Algorithm are identified. These parameters have a significant influence on machining characteristics such as MRR, EWR and surface roughness.