Optimization of surface roughness in milling using neural network (NN)

This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to pr...

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Main Author:	Ruzaimi, Zainon
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/1494/ http://umpir.ump.edu.my/id/eprint/1494/1/Ruzaimi_Zainon_%28_CD_5067_%29.pdf

id	ump-1494
recordtype	eprints
spelling	ump-14942015-03-03T07:51:24Z http://umpir.ump.edu.my/id/eprint/1494/ Optimization of surface roughness in milling using neural network (NN) Ruzaimi, Zainon TA Engineering (General). Civil engineering (General) This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to predict the resulting of surface roughness. The parameters of the variables are feed, cutting speed and depth of cut while the output is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where RSM approaches show 83.64% accuracy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. ANN technique shows 96.68% of accuracy which is feasible and applicable in the prediction value of Ra. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1494/1/Ruzaimi_Zainon_%28_CD_5067_%29.pdf Ruzaimi, Zainon (2010) Optimization of surface roughness in milling using neural network (NN). Faculty of Mechanical Engineering, Universiti Malaysia Pahang.
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) Ruzaimi, Zainon Optimization of surface roughness in milling using neural network (NN)
description	This thesis discuss the Optimization of surface roughness in milling using Artificial Neural Network (ANN).Response Surface Methodology (RSM) and Neural Network implemented to model the end milling process that are using coated carbide TiN as the cutting tool and aluminium 6061 as material due to predict the resulting of surface roughness. The parameters of the variables are feed, cutting speed and depth of cut while the output is surface roughness. The model is validated through a comparison of the experimental values with their predicted counterparts. A good agreement is found where RSM approaches show 83.64% accuracy which reliable to be use in Ra prediction and state the feed parameter is the most significant parameter followed by depth of cut and cutting speed influence the surface roughness. ANN technique shows 96.68% of accuracy which is feasible and applicable in the prediction value of Ra. The proved technique opens the door for a new, simple and efficient approach that could be applied to the calibration of other empirical models of machining.
format	Undergraduates Project Papers
author	Ruzaimi, Zainon
author_facet	Ruzaimi, Zainon
author_sort	Ruzaimi, Zainon
title	Optimization of surface roughness in milling using neural network (NN)
title_short	Optimization of surface roughness in milling using neural network (NN)
title_full	Optimization of surface roughness in milling using neural network (NN)
title_fullStr	Optimization of surface roughness in milling using neural network (NN)
title_full_unstemmed	Optimization of surface roughness in milling using neural network (NN)
title_sort	optimization of surface roughness in milling using neural network (nn)
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1494/ http://umpir.ump.edu.my/id/eprint/1494/1/Ruzaimi_Zainon_%28_CD_5067_%29.pdf
first_indexed	2023-09-18T21:54:40Z
last_indexed	2023-09-18T21:54:40Z
_version_	1777413999117205504

Optimization of surface roughness in milling using neural network (NN)

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