Erosion rate model comparison of electrical discharge machining process
This paper reports a comparison studies between two erosion rate model and experimental results of an Electrical Discharge Machining (EDM) process for high gap current. Two erosion rate models are Dimensional Analysis (DA) model and Artificial Neural Network (ANN) model that have been summarized fro...
Saved in:
Main Authors: | , , , |
---|---|
Format: | Book Section |
Published: |
IEEE
2012
|
Subjects: | |
Online Access: | http://eprints.utm.my/id/eprint/35765/ http://dx.doi.org/10.1109/ICIAS.2012.6306172 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.utm.35765 |
---|---|
record_format |
eprints |
spelling |
my.utm.357652017-02-04T06:43:06Z http://eprints.utm.my/id/eprint/35765/ Erosion rate model comparison of electrical discharge machining process Yahya, Azli Andromeda, Trias Abdul Rahim, Muhammad Arif Baharom, Ameruddin TK Electrical engineering. Electronics Nuclear engineering This paper reports a comparison studies between two erosion rate model and experimental results of an Electrical Discharge Machining (EDM) process for high gap current. Two erosion rate models are Dimensional Analysis (DA) model and Artificial Neural Network (ANN) model that have been summarized from the previous author's publication. The data analysis is based on a copper electrode and steel workpiece materials. The result indicated that the ANN model provides better accuracy than the DA model when compared to the experimental results. IEEE 2012 Book Section PeerReviewed Yahya, Azli and Andromeda, Trias and Abdul Rahim, Muhammad Arif and Baharom, Ameruddin (2012) Erosion rate model comparison of electrical discharge machining process. In: ICIAS 2012 - 2012 4th International Conference on Intelligent and Advanced Systems: A Conference of World Engineering, Science and Technology Congress (ESTCON) - Conference Proceedings. IEEE, New York, USA, pp. 122-125. ISBN 978-145771967-7 http://dx.doi.org/10.1109/ICIAS.2012.6306172 DOI:10.1109/ICIAS.2012.6306172 |
institution |
Universiti Teknologi Malaysia |
building |
UTM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Teknologi Malaysia |
content_source |
UTM Institutional Repository |
url_provider |
http://eprints.utm.my/ |
topic |
TK Electrical engineering. Electronics Nuclear engineering |
spellingShingle |
TK Electrical engineering. Electronics Nuclear engineering Yahya, Azli Andromeda, Trias Abdul Rahim, Muhammad Arif Baharom, Ameruddin Erosion rate model comparison of electrical discharge machining process |
description |
This paper reports a comparison studies between two erosion rate model and experimental results of an Electrical Discharge Machining (EDM) process for high gap current. Two erosion rate models are Dimensional Analysis (DA) model and Artificial Neural Network (ANN) model that have been summarized from the previous author's publication. The data analysis is based on a copper electrode and steel workpiece materials. The result indicated that the ANN model provides better accuracy than the DA model when compared to the experimental results. |
format |
Book Section |
author |
Yahya, Azli Andromeda, Trias Abdul Rahim, Muhammad Arif Baharom, Ameruddin |
author_facet |
Yahya, Azli Andromeda, Trias Abdul Rahim, Muhammad Arif Baharom, Ameruddin |
author_sort |
Yahya, Azli |
title |
Erosion rate model comparison of electrical discharge machining process |
title_short |
Erosion rate model comparison of electrical discharge machining process |
title_full |
Erosion rate model comparison of electrical discharge machining process |
title_fullStr |
Erosion rate model comparison of electrical discharge machining process |
title_full_unstemmed |
Erosion rate model comparison of electrical discharge machining process |
title_sort |
erosion rate model comparison of electrical discharge machining process |
publisher |
IEEE |
publishDate |
2012 |
url |
http://eprints.utm.my/id/eprint/35765/ http://dx.doi.org/10.1109/ICIAS.2012.6306172 |
_version_ |
1643649833355968512 |
score |
13.211869 |