Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel

Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel

In this work, 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 par...

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Main Authors: Patwari, Muhammed Anayet Ullah, Amin, A. K. M. Nurul, Faris, Waleed Fekry, Ginta, Turnad Lenggo, Alam, S., Lajis, M. A.
Format: Conference or Workshop Item
Language:English
Published: 2008
Subjects:
TS Manufactures
Online Access:http://irep.iium.edu.my/17330/
http://irep.iium.edu.my/17330/
http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf
id iium-17330
recordtype eprints
spelling iium-173302012-04-10T06:33:07Z http://irep.iium.edu.my/17330/ Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel Patwari, Muhammed Anayet Ullah Amin, A. K. M. Nurul Faris, Waleed Fekry Ginta, Turnad Lenggo Alam, S. Lajis, M. A. TS Manufactures In this work, 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 backpropagation 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. 2008-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf Patwari, Muhammed Anayet Ullah and Amin, A. K. M. Nurul and Faris, Waleed Fekry and Ginta, Turnad Lenggo and Alam, S. and Lajis, M. A. (2008) Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel. In: International Conference of Curtin University of Science and Technology Engineering (CUTSE 2008), 24 - 27 November 2008, Sarawak, Malaysia. http://www.curtin.edu.my/index.htm
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic TS Manufactures
spellingShingle TS Manufactures
Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Faris, Waleed Fekry
Ginta, Turnad Lenggo
Alam, S.
Lajis, M. A.
Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
description In this work, 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 backpropagation 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 Conference or Workshop Item
author Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Faris, Waleed Fekry
Ginta, Turnad Lenggo
Alam, S.
Lajis, M. A.
author_facet Patwari, Muhammed Anayet Ullah
Amin, A. K. M. Nurul
Faris, Waleed Fekry
Ginta, Turnad Lenggo
Alam, S.
Lajis, M. A.
author_sort Patwari, Muhammed Anayet Ullah
title Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
title_short Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
title_full Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
title_fullStr Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
title_full_unstemmed Development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
title_sort development of an artificial neural network model for the prediction of the chip serration frequency in end milling of medium carbon steel
publishDate 2008
url http://irep.iium.edu.my/17330/
http://irep.iium.edu.my/17330/
http://irep.iium.edu.my/17330/1/CUTSE_Sarawak.pdf
first_indexed 2023-09-18T20:26:10Z
last_indexed 2023-09-18T20:26:10Z
_version_ 1777408430925938688

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