Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network

Surface roughness is one of the most important properties in any machining process and in micro milling it is really critical as the product needs to be of a very high surface quality. Therefore the present research is aimed at finding the optimal process parameters for end milling process and op...

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Main Author:	Mohammed Saif, Yazid Abdulsameea
Format:	Thesis
Language:	English English
Published:	2014
Subjects:	TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice
Online Access:	http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf
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spelling	my-uthm-ep.17012021-10-04T08:44:20Z Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network 2014-06 Mohammed Saif, Yazid Abdulsameea TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice Surface roughness is one of the most important properties in any machining process and in micro milling it is really critical as the product needs to be of a very high surface quality. Therefore the present research is aimed at finding the optimal process parameters for end milling process and optimum surface roughness. In this study by using regression model and Artificial Neural Networks (ANN) which are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. The End milling process is a widely used machining process in aerospace industries and many other industries ranging from large manufacturers to a small tool and die shops, because of its versatility and efficiency. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the surface roughness was taken as the output. The obtained results proved the ability of ANN method for End milling process modeling and optimization. The final measurement experiment and predicting the error of surface roughness in neural network have been performed to verify the surface roughness optimum error percentage 1.71µm. For this study, the accuracy of artificial neural network and regression model 98.2% and 96.3 respectively. 2014-06 Thesis http://eprints.uthm.edu.my/1701/ http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf text en public http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Mekanikal dan Pembuatan
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English
topic	TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice
spellingShingle	TJ Mechanical engineering and machinery TJ1125-1345 Machine shops and machine shop practice Mohammed Saif, Yazid Abdulsameea Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
description	Surface roughness is one of the most important properties in any machining process and in micro milling it is really critical as the product needs to be of a very high surface quality. Therefore the present research is aimed at finding the optimal process parameters for end milling process and optimum surface roughness. In this study by using regression model and Artificial Neural Networks (ANN) which are widely used for both modeling and optimizing the performance of the manufacturing technologies. Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. The End milling process is a widely used machining process in aerospace industries and many other industries ranging from large manufacturers to a small tool and die shops, because of its versatility and efficiency. The present work involves the estimation of optimal values of the process variables like, speed, feed and depth of cut, whereas the surface roughness was taken as the output. The obtained results proved the ability of ANN method for End milling process modeling and optimization. The final measurement experiment and predicting the error of surface roughness in neural network have been performed to verify the surface roughness optimum error percentage 1.71µm. For this study, the accuracy of artificial neural network and regression model 98.2% and 96.3 respectively.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Mohammed Saif, Yazid Abdulsameea
author_facet	Mohammed Saif, Yazid Abdulsameea
author_sort	Mohammed Saif, Yazid Abdulsameea
title	Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
title_short	Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
title_full	Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
title_fullStr	Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
title_full_unstemmed	Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
title_sort	modelling and simulation of surface roughness obtain from micro milling by using artificial neural network
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Mekanikal dan Pembuatan
publishDate	2014
url	http://eprints.uthm.edu.my/1701/1/24p%20YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF.pdf http://eprints.uthm.edu.my/1701/2/YAZID%20ABDULSAMEEA%20MOHAMMED%20SAIF%20WATERMARK.pdf
_version_	1747830850357035008

Modelling and simulation of surface roughness obtain from micro milling by using artificial neural network

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