Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy

In this work, an artificial neural network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and surface roughness during high speed end milling of nickel-based Inconel 718 alloy. The input parameters of the ANN model are the cutting parame...

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Main Authors:	Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Anayet Ullah
Format:	Conference or Workshop Item
Language:	English
Published:	2008
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/1/paper_on_ICCCE08_PaperID696.pdf

id	iium-16512
recordtype	eprints
spelling	iium-165122012-09-19T08:15:54Z http://irep.iium.edu.my/16512/ Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Anayet Ullah TJ Mechanical engineering and machinery In this work, an artificial neural network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and surface roughness during high speed end milling of nickel-based Inconel 718 alloy. 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 surface roughness. 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 three-level full factorial experimental design technique. A predictive model for surface roughness 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 Inconel 718 alloy with single-layer PVD TiAlN 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 surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production. 2008 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/16512/1/paper_on_ICCCE08_PaperID696.pdf Hossain, Mohammad Ishtiyaq and Amin, A. K. M. Nurul and Patwari, Anayet Ullah (2008) Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy. In: International Conference on Computer and Communication Engineering 2008, 13-15 May 2008, Kuala Lumpur. http://dx.doi.org/10.1109/ICCCE.2008.4580819 doi:10.1109/ICCCE.2008.4580819
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Anayet Ullah Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
description	In this work, an artificial neural network (ANN) model was developed for the investigation and prediction of the relationship between cutting parameters and surface roughness during high speed end milling of nickel-based Inconel 718 alloy. 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 surface roughness. 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 three-level full factorial experimental design technique. A predictive model for surface roughness 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 Inconel 718 alloy with single-layer PVD TiAlN 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 surface roughness in metal-cutting operations and for the optimization of the surface roughness for efficient and economic production.
format	Conference or Workshop Item
author	Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Anayet Ullah
author_facet	Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Anayet Ullah
author_sort	Hossain, Mohammad Ishtiyaq
title	Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
title_short	Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
title_full	Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
title_fullStr	Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
title_full_unstemmed	Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
title_sort	development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
publishDate	2008
url	http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/ http://irep.iium.edu.my/16512/1/paper_on_ICCCE08_PaperID696.pdf
first_indexed	2023-09-18T20:25:18Z
last_indexed	2023-09-18T20:25:18Z
_version_	1777408376393695232

Development of an artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy

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