Artificial neural network chip serration frequency model in end milling of medium carbon steel

In this research, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting...

Full description

Bibliographic Details
Main Authors:	Patwari, Muhammed Anayet Ullah, Amin, A. K. M. Nurul, Faris, Waleed Fekry
Format:	Article
Language:	English
Published:	Scientific Research Publishing Company 2009
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/390/ http://irep.iium.edu.my/390/ http://irep.iium.edu.my/390/1/waleed_faris.pdf

id	iium-390
recordtype	eprints
spelling	iium-3902011-07-12T00:35:26Z http://irep.iium.edu.my/390/ Artificial neural network chip serration frequency model in end milling of medium carbon steel Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry TK Electrical engineering. Electronics Nuclear engineering In this research, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting parameters: cutting speed, feed and axial depth of cut. The output parameter of the model was chip serration frequency. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical central composite design experimental design technique. A predictive model for chip serration frequency was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated, when end milling steel with TiN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the chip serration frequency in metal-cutting operations Scientific Research Publishing Company 2009 Article PeerReviewed application/pdf en http://irep.iium.edu.my/390/1/waleed_faris.pdf Patwari, Muhammed Anayet Ullah and Amin, A. K. M. Nurul and Faris, Waleed Fekry (2009) Artificial neural network chip serration frequency model in end milling of medium carbon steel. Research Journal of Applied Sciences, 4 (3). pp. 108-112. ISSN 1815-932X http://www.medwelljournals.com/abstract/?doi=rjasci.2009.108.112
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Artificial neural network chip serration frequency model in end milling of medium carbon steel
description	In this research, an Artificial Neural Network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and chip serration frequency during high speed end milling of medium carbon steel (S45C). The input parameters of the ANN model are the cutting parameters: cutting speed, feed and axial depth of cut. The output parameter of the model was chip serration frequency. For this interpretation, advantages of statistical experimental design technique, experimental measurements, artificial neural network were exploited in an integrated manner. Cutting experiments are designed based on statistical central composite design experimental design technique. A predictive model for chip serration frequency was created using a feed-forward back-propagation neural network exploiting experimental data. The network was trained with pairs of inputs/outputs datasets generated, when end milling steel with TiN coated carbide inserts. A very good predicting performance of the neural network, in terms of concurrence with experimental data was attained. The model can be used for the analysis and prediction for the complex relationship between cutting conditions and the chip serration frequency in metal-cutting operations
format	Article
author	Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry
author_facet	Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry
author_sort	Patwari, Muhammed Anayet Ullah
title	Artificial neural network chip serration frequency model in end milling of medium carbon steel
title_short	Artificial neural network chip serration frequency model in end milling of medium carbon steel
title_full	Artificial neural network chip serration frequency model in end milling of medium carbon steel
title_fullStr	Artificial neural network chip serration frequency model in end milling of medium carbon steel
title_full_unstemmed	Artificial neural network chip serration frequency model in end milling of medium carbon steel
title_sort	artificial neural network chip serration frequency model in end milling of medium carbon steel
publisher	Scientific Research Publishing Company
publishDate	2009
url	http://irep.iium.edu.my/390/ http://irep.iium.edu.my/390/ http://irep.iium.edu.my/390/1/waleed_faris.pdf
first_indexed	2023-09-18T20:07:30Z
last_indexed	2023-09-18T20:07:30Z
_version_	1777407256281743360

Artificial neural network chip serration frequency model in end milling of medium carbon steel

Similar Items