kadhim, S., Mansor, K., Abbood, M. (2022). Prediction of Surface Quality in Electrical Discharge Machining Process for 7024 AL Alloy Using Artificial Neural Network Model. , 13(2), 106-113. doi: 10.37649/aengs.2022.176364
safaa kadhim; Khalida Kadhim Mansor; Mohanad Qusay Abbood. "Prediction of Surface Quality in Electrical Discharge Machining Process for 7024 AL Alloy Using Artificial Neural Network Model". , 13, 2, 2022, 106-113. doi: 10.37649/aengs.2022.176364
kadhim, S., Mansor, K., Abbood, M. (2022). 'Prediction of Surface Quality in Electrical Discharge Machining Process for 7024 AL Alloy Using Artificial Neural Network Model', , 13(2), pp. 106-113. doi: 10.37649/aengs.2022.176364
kadhim, S., Mansor, K., Abbood, M. Prediction of Surface Quality in Electrical Discharge Machining Process for 7024 AL Alloy Using Artificial Neural Network Model. , 2022; 13(2): 106-113. doi: 10.37649/aengs.2022.176364

Prediction of Surface Quality in Electrical Discharge Machining Process for 7024 AL Alloy Using Artificial Neural Network Model

Anbar Journal of Engineering Sciences
Article 13, Volume 13, Issue 2, Autumn 2022, Page 106-113  XML PDF (804 K)
Document Type: Research Paper
DOI: 10.37649/aengs.2022.176364
Authors
safaa kadhim 1; Khalida Kadhim Mansor2; Mohanad Qusay Abbood2
1Production Engineering and Metallurgy Department, University of Technology, Baghdad, iraq
2Department of Production Engineering and Metallurgy / University of Technology / Iraq
Abstract
In this article, an experimental study of the single-pass hybrid (PV/T) collector is conducted in the climatic conditions of Fallujah city, where the experimental results are compared with a previous research to validate the results. The effect of changing the angle of inclination of the hybrid collector (PV/T) and its effect on the electrical power in the range (20°-50°) is studied. The optimum angle of the collector is found to be 30°, which gives a maximum electrical power of 58.8 W at average solar radiation of 734.35 W/m2. In another experimental study with different air flow rates ranged from 0.04 kg/s to 0163 kg/s, where it is found that the maximum electrical power of 57.66 W at an air flow rate of 0.135 kg/s, while the maximum thermal efficiency reaches 33.53% at an air flow of 0.163 kg/s at average solar radiation of 786 W/m2.
 
Keywords
Surface Roughness; electrical discharge; Artificial Neural Network Model
References
[1]         Pravin R. Kubade and V. S. Jadhav, “An Experimental Investigation of Electrode Wear Rate (EWR), Material Removal Rate (MRR) and Radial Overcut (ROC) in EDM of High Carbon-High Chromium Steel (AISI D3)”, International Journal of Engineering and Advanced Technology, Volume-1, Issue-5, June, 2012.

[2]         Ho K. H., and Newman S. T., “State of the art electrical discharge machining (EDM)”, International Journal of Machine Tools and Manufacture, Vol. 43, No. pp. 1287-1300, 2003.

[3]         Tsai, K. M. and Wang, P. J., “Predictions on surface finish in Electrical Discharge Machining based upon neural network models”, International Journal of Machine Tools & Manufacture, Vol.41, pp.1385-1403, 2001

[4]         Datta, R. N., “Machine tools”, New Central Book Agency Ltd , London, Vol.2, pp. 282-286, 2010.

[5]         Puertas, I. and Luis, C. J., “Optimization of EDM conditions in the manufacturing process of B4C and WC-Co conductive ceramics”, Int. J. Adv. Manuf. Technol., 2011.

[6]         Shishir Mohan Shrivastava and Dr. A.K.Sarathe, "Influence of Process Parameters and Electrode Shape Configuration  On           Material Removal Rate, Surface Roughness And Electrode Wear In Die Sinking EDM: A             Review",     International Journal of Emerging Technology and Advanced Engineering, Vol. 4, pp. 138-145, 2014.

[7]         Nibu Mathew, Dinesh Kumar, Naveen Beri and Anil Kumar, "Study of material removal rate of different tool materials during EDM of H11 steel    at             reverse  polarity", International Journal of Advanced Engineering Technology, Vol. V , pp. 25- 30 ,2014.

[8]         Lei Li, Lin Gu, Xuecheng Xi, Wansheng Zhao, “Influence of flushing on performance of EDM with bunched electrode”, Int J Adv Manuf Technol , Vol.58, pp.187-194, 2012.

[9]         Mahendran S., Devarajan R., Nagarajan T., Majdi A., “A Review of Micro-EDM”, Proceedings of the International Multiconference of Engineers and Computer Scientists, Vol.2, 2010.

[10]      Oleghe, O. “A predictive noise correction methodology for manufacturing process datasets”. J Big Data 7, 89, 2020.

[11]      Khan, M.Y., Rao, P.S., Pabla, B.S, “Investigations on the feasibility of jatropha curcas oil based biodiesel for sustainable dielectric fluid in EDM process”. Mat.Tod.: Proc. 26, 335–340, 2020.

[12]      Hasan. O, Tuncay. E and Fehmi. E, "Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm",Materials and Design, vol.27, pp.735–744, 2006.

[13]      Babur. O ,Hasan. O and Hasan. K, "Optimum surface roughness in end milling Inconel 718 by coupling neural network model and genetic algorithm",         Int J Adv Manuf Technol,vol.27,pp.234–241,2005.

[14]      Prakasvudhisarn. S, Kunnapapdeelert, and Yenradee. P," Optimal cutting condition determination for desired surface roughness in end milling", Int J Adv Manuf Technol, vol. 41, pp.440 - 451, 2009.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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