Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method

Electrical discharge machining (EDM) is one of the non-traditional machining processes normally used in manufacturing very hard materials that are electrically conductive. Tool electrodes form one of the main components of the machining system. The major properties that determine the suitability...

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Main Authors:	Ndaliman, Mohammed Baba, Al Hazza, Muataz Hazza Faizi, Khan, Ahsan Ali, Yeakub Ali, Mohammad
Format:	Article
Language:	English
Published:	American-Eurasian Network for Scientific Information (AENSI Publisher) 2012
Subjects:	T Technology (General)
Online Access:	http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/1/EDM%201%282012%29.pdf

id	iium-55331
recordtype	eprints
spelling	iium-553312017-07-18T08:28:01Z http://irep.iium.edu.my/55331/ Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method Ndaliman, Mohammed Baba Al Hazza, Muataz Hazza Faizi Khan, Ahsan Ali Yeakub Ali, Mohammad T Technology (General) Electrical discharge machining (EDM) is one of the non-traditional machining processes normally used in manufacturing very hard materials that are electrically conductive. Tool electrodes form one of the main components of the machining system. The major properties that determine the suitability of such electrodes are electrical conductivity, thermal conductivity and density. The objective of this paper is to present the use of Artificial Neural Network (ANN) architecture in modeling these properties. In the research, Cu-TaC electrode compacts were produced at two levels each of the composition and the compacting pressures from copper and tantalum carbide powders for use in EDM. The compositions of the Cu-TaC are made of 30 % and 55 % wt of TaC, while the compacting pressures are 1, 500 psi and 3,000 psi. They were subjected to sintering at temperatures of 450°C and 850 °C. The properties were measured before and after sintering. Results showed that the sintered electrodes are not suitable for EDM because they lost their electrical conductivity. The presintered electrodes (green compacts) were however found to suitable for EDM. Artificial neural network technique with 16 experimental runs was used to develop the new models for predicting the electrical conductivity, thermal conductivity and density of the green compacted electrodes. The models were been built by using MATLAB 2009b. Results show that ANN models are capable of predicting the electrode properties with high degree of prediction accuracy compared to the experimental results American-Eurasian Network for Scientific Information (AENSI Publisher) 2012-12 Article PeerReviewed application/pdf en http://irep.iium.edu.my/55331/1/EDM%201%282012%29.pdf Ndaliman, Mohammed Baba and Al Hazza, Muataz Hazza Faizi and Khan, Ahsan Ali and Yeakub Ali, Mohammad (2012) Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method. Australian Journal of Basic and Applied Sciences, 6 (13). pp. 192-199. ISSN 1991-8178 http://www.ajbasweb.com/old/ajbas/2012/December%202012/192-199.pdf
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) Ndaliman, Mohammed Baba Al Hazza, Muataz Hazza Faizi Khan, Ahsan Ali Yeakub Ali, Mohammad Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
description	Electrical discharge machining (EDM) is one of the non-traditional machining processes normally used in manufacturing very hard materials that are electrically conductive. Tool electrodes form one of the main components of the machining system. The major properties that determine the suitability of such electrodes are electrical conductivity, thermal conductivity and density. The objective of this paper is to present the use of Artificial Neural Network (ANN) architecture in modeling these properties. In the research, Cu-TaC electrode compacts were produced at two levels each of the composition and the compacting pressures from copper and tantalum carbide powders for use in EDM. The compositions of the Cu-TaC are made of 30 % and 55 % wt of TaC, while the compacting pressures are 1, 500 psi and 3,000 psi. They were subjected to sintering at temperatures of 450°C and 850 °C. The properties were measured before and after sintering. Results showed that the sintered electrodes are not suitable for EDM because they lost their electrical conductivity. The presintered electrodes (green compacts) were however found to suitable for EDM. Artificial neural network technique with 16 experimental runs was used to develop the new models for predicting the electrical conductivity, thermal conductivity and density of the green compacted electrodes. The models were been built by using MATLAB 2009b. Results show that ANN models are capable of predicting the electrode properties with high degree of prediction accuracy compared to the experimental results
format	Article
author	Ndaliman, Mohammed Baba Al Hazza, Muataz Hazza Faizi Khan, Ahsan Ali Yeakub Ali, Mohammad
author_facet	Ndaliman, Mohammed Baba Al Hazza, Muataz Hazza Faizi Khan, Ahsan Ali Yeakub Ali, Mohammad
author_sort	Ndaliman, Mohammed Baba
title	Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
title_short	Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
title_full	Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
title_fullStr	Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
title_full_unstemmed	Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method
title_sort	development of a new model for predicting edm properties of cu-tac compact electrodes based on artificial neural network method
publisher	American-Eurasian Network for Scientific Information (AENSI Publisher)
publishDate	2012
url	http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/ http://irep.iium.edu.my/55331/1/EDM%201%282012%29.pdf
first_indexed	2023-09-18T21:18:13Z
last_indexed	2023-09-18T21:18:13Z
_version_	1777411705500860416

Development of a new model for predicting EDM properties of Cu-TaC compact electrodes based on artificial neural network method

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