Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

Surface roughness is one of the important factors for evaluating workpiece quality during the machining process because the quality of surface roughness affects the functional characteristics of the workpiece such as compatibility, fatigue resistance and surface friction. The factors that affect...

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Main Authors:	Hossain, Mohammad Ishtiyaq, Amin, A. K. M. Nurul, Patwari, Muhammed Anayet Ullah
Format:	Book Chapter
Language:	English
Published:	IIUM university press 2011
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://irep.iium.edu.my/23598/ http://irep.iium.edu.my/23598/ http://irep.iium.edu.my/23598/4/chp19.pdf

id	iium-23598
recordtype	eprints
spelling	iium-235982012-09-12T01:01:04Z http://irep.iium.edu.my/23598/ 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, Muhammed Anayet Ullah TJ Mechanical engineering and machinery Surface roughness is one of the important factors for evaluating workpiece quality during the machining process because the quality of surface roughness affects the functional characteristics of the workpiece such as compatibility, fatigue resistance and surface friction. The factors that affect the surface roughness during the end milling process include tool geometry, feed rate, depth of cut and cutting speed. Several researchers have studied the end milling process in the recent years. The researchers also used response surface methodology (RSM) to explore the effect of cutting parameters as cutting speed, feed rate and axial depth of cut. Alauddin et al. [1] developed a mathematical model to predict the surface roughness of steel after end milling. The prediction model was expressed via cutting speed, feed rate and depth of cut. Fuh and Hwang [2] used RSM to construct a model that can predict the milling force in end milling operations. But as the machining process is nonlinear and time-dependent, it is difficult for the traditional identification methods to provide an accurate model. Compared to traditional computing methods, the artificial neural networks (ANNs) are robust and global. ANNs have the characteristics of universal approximation, parallel distributed processing, hardware implementation, learning and adaptation, and multivariable systems [3]. ANNs have been extensively applied in modeling many metal-cutting operations such as turning, milling, and drilling [4-5]. However, this study was inspired by the very limited work on the application of ANNs in modeling the relationship between cutting conditions and the surface roughness during high-speed end milling of nickel-based, Inconel 718 alloy. IIUM university press 2011 Book Chapter PeerReviewed application/pdf en http://irep.iium.edu.my/23598/4/chp19.pdf Hossain, Mohammad Ishtiyaq and Amin, A. K. M. Nurul and Patwari, Muhammed Anayet Ullah (2011) Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy. In: Advanced Machining Towards Improved Machinability of Difficult-to-Cut Materials. IIUM university press, International Islamic University Malaysia, Kuala Lumpur, Malaysia, pp. 149-154. ISBN 9789674181758 http://rms.research.iium.edu.my/bookstore/default.aspx
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, Muhammed Anayet Ullah Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
description	Surface roughness is one of the important factors for evaluating workpiece quality during the machining process because the quality of surface roughness affects the functional characteristics of the workpiece such as compatibility, fatigue resistance and surface friction. The factors that affect the surface roughness during the end milling process include tool geometry, feed rate, depth of cut and cutting speed. Several researchers have studied the end milling process in the recent years. The researchers also used response surface methodology (RSM) to explore the effect of cutting parameters as cutting speed, feed rate and axial depth of cut. Alauddin et al. [1] developed a mathematical model to predict the surface roughness of steel after end milling. The prediction model was expressed via cutting speed, feed rate and depth of cut. Fuh and Hwang [2] used RSM to construct a model that can predict the milling force in end milling operations. But as the machining process is nonlinear and time-dependent, it is difficult for the traditional identification methods to provide an accurate model. Compared to traditional computing methods, the artificial neural networks (ANNs) are robust and global. ANNs have the characteristics of universal approximation, parallel distributed processing, hardware implementation, learning and adaptation, and multivariable systems [3]. ANNs have been extensively applied in modeling many metal-cutting operations such as turning, milling, and drilling [4-5]. However, this study was inspired by the very limited work on the application of ANNs in modeling the relationship between cutting conditions and the surface roughness during high-speed end milling of nickel-based, Inconel 718 alloy.
format	Book Chapter
author	Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Muhammed Anayet Ullah
author_facet	Hossain, Mohammad Ishtiyaq Amin, A. K. M. Nurul Patwari, Muhammed Anayet Ullah
author_sort	Hossain, Mohammad Ishtiyaq
title	Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
title_short	Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
title_full	Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
title_fullStr	Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
title_full_unstemmed	Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy
title_sort	artificial neural network algorithm for predicting the surface roughness in end milling of inconel 718 alloy
publisher	IIUM university press
publishDate	2011
url	http://irep.iium.edu.my/23598/ http://irep.iium.edu.my/23598/ http://irep.iium.edu.my/23598/4/chp19.pdf
first_indexed	2023-09-18T20:35:39Z
last_indexed	2023-09-18T20:35:39Z
_version_	1777409027854041088

Artificial neural network algorithm for predicting the surface roughness in end milling of Inconel 718 alloy

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