Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method

Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel. Use of optimal ECM process conditions can significan...

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Autor principal:	Abbas Fadhil Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2017
Materias:	ECM Carbon MRR Ra Taguchi Method Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/dfaf3a7dcacc42f0883b3eecf0cc4f33
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spelling	oai:doaj.org-article:dfaf3a7dcacc42f0883b3eecf0cc4f332021-12-02T03:50:44ZPrediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method10.22153/kej.2016.06.0011818-11712312-0789https://doaj.org/article/dfaf3a7dcacc42f0883b3eecf0cc4f332017-12-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/321https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel. Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Signal-to-noise (S/N), the analysis of variance (ANOVA) was employed to find the optimal levels and to analyze the effect of electrochemical machining parameters on Ra and MRR. The surface roughness of the workpiece was decreased with the increase in current values and electrolyte concentration while causing an increase in material removal rate. The ability of the independent values to predict the dependent values (R2) were 87.5% and 96.3% for mean surface roughness and material removal rate, respectively. Abbas Fadhil IbrahimAl-Khwarizmi College of Engineering – University of BaghdadarticleECMCarbonMRRRaTaguchi MethodChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 12, Iss 4 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	ECM Carbon MRR Ra Taguchi Method Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	ECM Carbon MRR Ra Taguchi Method Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Abbas Fadhil Ibrahim Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
description	Electrochemical machining is one of the widely used non-conventional machining processes to machine complex and difficult shapes for electrically conducting materials, such as super alloys, Ti-alloys, alloy steel, tool steel and stainless steel. Use of optimal ECM process conditions can significantly reduce the ECM operating, tooling, and maintenance cost and can produce components with higher accuracy. This paper studies the effect of process parameters on surface roughness (Ra) and material removal rate (MRR), and the optimization of process conditions in ECM. Experiments were conducted based on Taguchi’s L9 orthogonal array (OA) with three process parameters viz. current, electrolyte concentration, and inter-electrode gap. Signal-to-noise (S/N), the analysis of variance (ANOVA) was employed to find the optimal levels and to analyze the effect of electrochemical machining parameters on Ra and MRR. The surface roughness of the workpiece was decreased with the increase in current values and electrolyte concentration while causing an increase in material removal rate. The ability of the independent values to predict the dependent values (R2) were 87.5% and 96.3% for mean surface roughness and material removal rate, respectively.
format	article
author	Abbas Fadhil Ibrahim
author_facet	Abbas Fadhil Ibrahim
author_sort	Abbas Fadhil Ibrahim
title	Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
title_short	Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
title_full	Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
title_fullStr	Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
title_full_unstemmed	Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method
title_sort	prediction of surface roughness and material removal rate in electrochemical machining using taguchi method
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2017
url	https://doaj.org/article/dfaf3a7dcacc42f0883b3eecf0cc4f33
work_keys_str_mv	AT abbasfadhilibrahim predictionofsurfaceroughnessandmaterialremovalrateinelectrochemicalmachiningusingtaguchimethod
_version_	1718401617521278976

Prediction of Surface Roughness and Material Removal Rate in Electrochemical Machining Using Taguchi Method

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