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

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Shukry H. Aghdeab Nareen Hafidh Obaeed Marwa Qasim Ibraheem

Abstract

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

Article Details

How to Cite
AGHDEAB, Shukry H.; OBAEED, Nareen Hafidh; IBRAHEEM, Marwa Qasim. Surface Roughness Prediction for Steel 304 In Edm Using Response Graph Modeling. Al-Khwarizmi Engineering Journal, [S.l.], v. 14, n. 4, p. 115- 124, nov. 2018. ISSN 2312-0789. Available at: <http://alkej.com/index.php/en/article/view/747>. Date accessed: 12 dec. 2018. doi: https://doi.org/10.22153/https://doi.org/10.22153/kej.2018.05.006.
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References

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