Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis

Powder Mixed Electro-Discharge Machining (PMEDM) is a hybrid machining process where a conductive powder is mixed to the dielectric fluid to facilitate effective machining of advanced material. In the present work application of Taguchi method in combination with Technique for order of preference by...

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Main Authors: S. Tripathy, D.K. Tripathy
Format: Article
Language:English
Published: Elsevier 2016-03-01
Series:Engineering Science and Technology, an International Journal
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2215098615001135
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spelling doaj-6f2676c3015149378284eff074c6b28e2020-11-24T23:14:53ZengElsevierEngineering Science and Technology, an International Journal2215-09862016-03-01191627010.1016/j.jestch.2015.07.010Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysisS. Tripathy0D.K. Tripathy1Mechanical Engineering Department, ITER, S‘O'A University, Bhubaneswar 751030, IndiaPro-Vice Chancellor, KIIT University, Bhubaneswar 751024, IndiaPowder Mixed Electro-Discharge Machining (PMEDM) is a hybrid machining process where a conductive powder is mixed to the dielectric fluid to facilitate effective machining of advanced material. In the present work application of Taguchi method in combination with Technique for order of preference by similarity to ideal solution (TOPSIS) and Grey Relational Analysis (GRA) have been adopted to evaluate the effectiveness of optimizing multiple performance characteristics for PMEDM of H-11 die steel using copper electrode. The effect of process variables such as powder concentration (Cp), peak current (Ip), pulse on time (Ton), duty cycle (DC) and gap voltage (Vg) on response parameters such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Electrode Wear Ratio (EWR) and Surface Roughness (SR) have been investigated using chromium powder mixed to the dielectric fluid. Analysis of variance (ANOVA) and F-test were performed to determine the significant parameters at a 95% confidence interval. Predicted results have been verified by confirmatory tests which show an improvement of 0.161689 and 0.2593 in the preference values using TOPSIS and GRA respectively. The recommended settings of process parameters is found to be Cp = 6 g/l, Ip = 6Amp, Ton = 100 µs, DC = 90% and Vg = 50 V from TOPSIS and Cp = 6 g/l, Ip = 3Amp, Ton = 150 µs, DC = 70% and Vg = 30 V from GRA. The microstructure analysis has been done for the optimal sample using Scanning Electron Microscope (SEM).http://www.sciencedirect.com/science/article/pii/S2215098615001135Powder mixed electric discharge machiningH-11 die steelTaguchiMulti-attribute optimizationGrey relational analysisTOPSIS
collection DOAJ
language English
format Article
sources DOAJ
author S. Tripathy
D.K. Tripathy
spellingShingle S. Tripathy
D.K. Tripathy
Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
Engineering Science and Technology, an International Journal
Powder mixed electric discharge machining
H-11 die steel
Taguchi
Multi-attribute optimization
Grey relational analysis
TOPSIS
author_facet S. Tripathy
D.K. Tripathy
author_sort S. Tripathy
title Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
title_short Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
title_full Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
title_fullStr Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
title_full_unstemmed Multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using TOPSIS and grey relational analysis
title_sort multi-attribute optimization of machining process parameters in powder mixed electro-discharge machining using topsis and grey relational analysis
publisher Elsevier
series Engineering Science and Technology, an International Journal
issn 2215-0986
publishDate 2016-03-01
description Powder Mixed Electro-Discharge Machining (PMEDM) is a hybrid machining process where a conductive powder is mixed to the dielectric fluid to facilitate effective machining of advanced material. In the present work application of Taguchi method in combination with Technique for order of preference by similarity to ideal solution (TOPSIS) and Grey Relational Analysis (GRA) have been adopted to evaluate the effectiveness of optimizing multiple performance characteristics for PMEDM of H-11 die steel using copper electrode. The effect of process variables such as powder concentration (Cp), peak current (Ip), pulse on time (Ton), duty cycle (DC) and gap voltage (Vg) on response parameters such as Material Removal Rate (MRR), Tool Wear Rate (TWR), Electrode Wear Ratio (EWR) and Surface Roughness (SR) have been investigated using chromium powder mixed to the dielectric fluid. Analysis of variance (ANOVA) and F-test were performed to determine the significant parameters at a 95% confidence interval. Predicted results have been verified by confirmatory tests which show an improvement of 0.161689 and 0.2593 in the preference values using TOPSIS and GRA respectively. The recommended settings of process parameters is found to be Cp = 6 g/l, Ip = 6Amp, Ton = 100 µs, DC = 90% and Vg = 50 V from TOPSIS and Cp = 6 g/l, Ip = 3Amp, Ton = 150 µs, DC = 70% and Vg = 30 V from GRA. The microstructure analysis has been done for the optimal sample using Scanning Electron Microscope (SEM).
topic Powder mixed electric discharge machining
H-11 die steel
Taguchi
Multi-attribute optimization
Grey relational analysis
TOPSIS
url http://www.sciencedirect.com/science/article/pii/S2215098615001135
work_keys_str_mv AT stripathy multiattributeoptimizationofmachiningprocessparametersinpowdermixedelectrodischargemachiningusingtopsisandgreyrelationalanalysis
AT dktripathy multiattributeoptimizationofmachiningprocessparametersinpowdermixedelectrodischargemachiningusingtopsisandgreyrelationalanalysis
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