Bi-objective Optimization of Process Parameters in Electric Discharge Machining of SS630 Using Grey Relation Analysis

One of the most former and widely utilized unconventional machining is known as the process of electric discharge machining (EDM). This is a procedure, where a serialized set of electric discharges are applied to eliminate the material from a workpiece (i.e., electrically conductive). This article p...

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Bibliographic Details
Published inJournal of nanomaterials Vol. 2022; no. 1
Main Authors Jampana, Venkata N. Raju, Ramana Rao, P. S. V., Kumar, Anubhav
Format Journal Article
LanguageEnglish
Published New York Hindawi 2022
Hindawi Limited
Subjects
Die sinking
Electric discharge machining
Electrodes
Experimentation
Finite element method
Heat conductivity
Manufacturing
Material removal rate (machining)
Nanomaterials
Neural networks
Numerical analysis
Optimization
Optimization techniques
Process parameters
Stainless steels
Steel
Surface roughness
Variables
Workpieces
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Summary:One of the most former and widely utilized unconventional machining is known as the process of electric discharge machining (EDM). This is a procedure, where a serialized set of electric discharges are applied to eliminate the material from a workpiece (i.e., electrically conductive). This article proposes a bi-objective optimization of process arguments in machining of stainless steel of 630 (SS630) grade using both experimentation and thermal investigation. Here, flushing pressure (PF), pulse on-time (TON), peak current (Ip), and pulse off-time (TOFF) are assumed as the input process parameters to conduct a series of experiments on die-sinking EDM (DS-EDM) of SS630 to predict the output characteristics of machining optimization using grey relation analysis (GRA). Both material removal rate (MRR) and surface roughness (SR) are used for evaluating the output response obtained using the machining performance. In addition, thermal investigation is performed using finite element method (FEM) analysis to compute theoretical MRR (T-MRR) for the distribution of temperature on workpiece. Further, GRA is employed to obtain the optimal process parameter combination for best output responses. From the confirmation test results, the optimal combination obtained using GRA approach is at Ip=6A, TON=35 μs, TOFF=90 μs, and PF=4 MPa.
ISSN:1687-4110
1687-4129
DOI:10.1155/2022/1165750