Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling

Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electri...

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Autores principales:	Shukry H. Aghdeab, Nareen Hafidh Obaeed, Marwa Qasim Ibraheem
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2018
Materias:	Electrical discharge machining, Graph Modeling, Response, Surface roughness Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/2666f68b2c364f41a99f975e9e58b126
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spelling	oai:doaj.org-article:2666f68b2c364f41a99f975e9e58b1262021-12-02T10:50:05ZSurface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling10.22153/kej.2018.05.0061818-11712312-0789https://doaj.org/article/2666f68b2c364f41a99f975e9e58b1262018-11-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/64https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on the surface roughness in the present research. 27 samples were run by using CNC-EDM machine which used for cutting steel 304 with dielectric solution of gas oil by supplied DC current values (10, 20, and 30A). Voltage of (140V) uses to cut 1.7mm thickness of the steel and use the copper electrode. The result from this work is useful to be implemented in industry to reduce the time and cost of Ra prediction. It is observed from response table and response graph that the applied current and pulse on time have the most influence parameters of surface roughness while pulse off time has less influence parameter on it. The supreme and least surface roughness, which is achieved from all the 27 experiments is (4.02 and 2.12µm), respectively. The qualitative assessment reveals that the surface roughness increases as the applied current and pulse on time increases Shukry H. AghdeabNareen Hafidh ObaeedMarwa Qasim IbraheemAl-Khwarizmi College of Engineering – University of BaghdadarticleElectrical discharge machining, Graph Modeling, Response, Surface roughnessChemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 14, Iss 4 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Electrical discharge machining, Graph Modeling, Response, Surface roughness Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Electrical discharge machining, Graph Modeling, Response, Surface roughness Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Shukry H. Aghdeab Nareen Hafidh Obaeed Marwa Qasim Ibraheem Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
description	Electrical Discharge Machining (EDM) is a non-traditional cutting technique for metals removing which is relied upon the basic fact that negligible tool force is produced during the machining process. Also, electrical discharge machining is used in manufacturing very hard materials that are electrically conductive. Regarding the electrical discharge machining procedure, the most significant factor of the cutting parameter is the surface roughness (Ra). Conventional try and error method is time consuming as well as high cost. The purpose of the present research is to develop a mathematical model using response graph modeling (RGM). The impact of various parameters such as (current, pulsation on time and pulsation off time) are studied on the surface roughness in the present research. 27 samples were run by using CNC-EDM machine which used for cutting steel 304 with dielectric solution of gas oil by supplied DC current values (10, 20, and 30A). Voltage of (140V) uses to cut 1.7mm thickness of the steel and use the copper electrode. The result from this work is useful to be implemented in industry to reduce the time and cost of Ra prediction. It is observed from response table and response graph that the applied current and pulse on time have the most influence parameters of surface roughness while pulse off time has less influence parameter on it. The supreme and least surface roughness, which is achieved from all the 27 experiments is (4.02 and 2.12µm), respectively. The qualitative assessment reveals that the surface roughness increases as the applied current and pulse on time increases
format	article
author	Shukry H. Aghdeab Nareen Hafidh Obaeed Marwa Qasim Ibraheem
author_facet	Shukry H. Aghdeab Nareen Hafidh Obaeed Marwa Qasim Ibraheem
author_sort	Shukry H. Aghdeab
title	Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
title_short	Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
title_full	Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
title_fullStr	Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
title_full_unstemmed	Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling
title_sort	surface roughness prediction for steel 304 in edm using response graph modeling
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2018
url	https://doaj.org/article/2666f68b2c364f41a99f975e9e58b126
work_keys_str_mv	AT shukryhaghdeab surfaceroughnesspredictionforsteel304inedmusingresponsegraphmodeling AT nareenhafidhobaeed surfaceroughnesspredictionforsteel304inedmusingresponsegraphmodeling AT marwaqasimibraheem surfaceroughnesspredictionforsteel304inedmusingresponsegraphmodeling
_version_	1718396576778420224

Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling

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