Investigating the effect of adding powders into dielectric in EDMmachining of Inconel 718 Alloy and using an ANN model to predictthe output parameters

Publish Year: 1396
نوع سند: مقاله کنفرانسی
زبان: English
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MECHAERO02_181

تاریخ نمایه سازی: 13 شهریور 1396

Abstract:

One of the promising methods for improving output parameters in electrical discharge machining (EDM) is the powder mixed electrical discharge machining (PMEDM) process. In this process, the powder of a conductive or non-conductive material is added to the dielectric fluid. EDM is a very complex process which is influenced by many parameters. By adding powder to the dielectric, the complexity of processincreases so that the determination of relations between input and output parameters becomes more difficult. In this paper, the effect of adding aluminum and silicon carbide powders to the dielectric on the output parameters of EDM process of Inconel 718 alloy is experimentally investigated. According to the results, both powders improve the EDM process performance. Moreover, an artificial neural network(ANN) model is developed for prediction of surface roughness (Ra) and material removal rate (MRR) and then the results are compared to a regression model. Results show that the accuracy of predicted Ra and MRR by ANN is higher than that of regression model, as the prediction errors are 5.22% and 9.16% for ANN and regression models, respectively

Keywords:

Electrical discharge machining (EDM) , Powder , Neural network , Regression , Inconel 718

Authors

Soroush Masoudi

Young Researchers and Elite Club, Najafabad Branch, Islamic Azad University, Najafabad, Iran

Seid Ali Mirsoleimani

Department of Mechanical Engineering, Isfahan University of Technology, Isfahan ۸۴۱۵۶-۸۳۱۱۱, Iran

Ali Najafi

Department of Mechanical Engineering, Isfahan University of Technology, Isfahan ۸۴۱۵۶-۸۳۱۱۱, Iran

Ana Vafadar

School of Engineering, Edith Cowan University, Perth, Western Australia