Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method

Electrical discharge machining (EDM) is a very complex and stochastic process. Thus, it is difficult to predict or to estimate its output characteristics accurately by mathematical models. Therefore, the non-conventional techniques for modeling become more effective. In this research, the Artificial...

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Main Authors:	Al Hazza, Muataz Hazza Faizi, Mohammed , Baba Ndaliman, muhammad, Hasibul hasan, Mohammad , Yeakub Ali, Khan, Ahsan Ali
Format:	Article
Language:	English
Published:	Praise Worthy Prize S.r.l. 2013
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/35322/ http://irep.iium.edu.my/35322/ http://irep.iium.edu.my/35322/1/030-Al_Hazza_def_13574_.pdf

id	iium-35322
recordtype	eprints
spelling	iium-353222014-02-17T01:01:36Z http://irep.iium.edu.my/35322/ Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method Al Hazza, Muataz Hazza Faizi Mohammed , Baba Ndaliman muhammad, Hasibul hasan Mohammad , Yeakub Ali Khan, Ahsan Ali T Technology (General) Electrical discharge machining (EDM) is a very complex and stochastic process. Thus, it is difficult to predict or to estimate its output characteristics accurately by mathematical models. Therefore, the non-conventional techniques for modeling become more effective. In this research, the Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the surface roughness (SR), Material Removal Rate (MRR) and Tool Wear Rate (TWR). The EDM performance of Cu compact electrode have been investigated with peak current (Ip), pulse duration (ton) and duty factor (η) as the input variables. A set of experimental data was obtained with different levels. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors at five levels. The neural network model was built by using MATLAB. The results indicate that even with the complexity of developing a model and predicting the results in EDM process, the neural network technique is found to be adequate in predicting the SR, MRR and TWR. Predictive neural network models are found to be capable to give high accuracy. Praise Worthy Prize S.r.l. 2013-11 Article PeerReviewed application/pdf en http://irep.iium.edu.my/35322/1/030-Al_Hazza_def_13574_.pdf Al Hazza, Muataz Hazza Faizi and Mohammed , Baba Ndaliman and muhammad, Hasibul hasan and Mohammad , Yeakub Ali and Khan, Ahsan Ali (2013) Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method. International Review of Mechanical Engineering, 7 (7). pp. 1464-1470. ISSN 1970-8734 http://www.praiseworthyprize.com/IREME.htm
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	T Technology (General)
spellingShingle	T Technology (General) Al Hazza, Muataz Hazza Faizi Mohammed , Baba Ndaliman muhammad, Hasibul hasan Mohammad , Yeakub Ali Khan, Ahsan Ali Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
description	Electrical discharge machining (EDM) is a very complex and stochastic process. Thus, it is difficult to predict or to estimate its output characteristics accurately by mathematical models. Therefore, the non-conventional techniques for modeling become more effective. In this research, the Artificial Neural Network (ANN) was applied as an effective tool for modeling and predicting the surface roughness (SR), Material Removal Rate (MRR) and Tool Wear Rate (TWR). The EDM performance of Cu compact electrode have been investigated with peak current (Ip), pulse duration (ton) and duty factor (η) as the input variables. A set of experimental data was obtained with different levels. The experiments were planned and implemented using Central Composites Design (CCD) of Response Surface Methodology (RSM) with three input factors at five levels. The neural network model was built by using MATLAB. The results indicate that even with the complexity of developing a model and predicting the results in EDM process, the neural network technique is found to be adequate in predicting the SR, MRR and TWR. Predictive neural network models are found to be capable to give high accuracy.
format	Article
author	Al Hazza, Muataz Hazza Faizi Mohammed , Baba Ndaliman muhammad, Hasibul hasan Mohammad , Yeakub Ali Khan, Ahsan Ali
author_facet	Al Hazza, Muataz Hazza Faizi Mohammed , Baba Ndaliman muhammad, Hasibul hasan Mohammad , Yeakub Ali Khan, Ahsan Ali
author_sort	Al Hazza, Muataz Hazza Faizi
title	Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
title_short	Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
title_full	Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
title_fullStr	Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
title_full_unstemmed	Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method
title_sort	modeling the electrical parameters in edm process of ti6al4v alloy using neural network method
publisher	Praise Worthy Prize S.r.l.
publishDate	2013
url	http://irep.iium.edu.my/35322/ http://irep.iium.edu.my/35322/ http://irep.iium.edu.my/35322/1/030-Al_Hazza_def_13574_.pdf
first_indexed	2023-09-18T20:50:39Z
last_indexed	2023-09-18T20:50:39Z
_version_	1777409971732873216

Modeling the electrical parameters in EDM process of Ti6Al4V alloy using Neural Network method

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