Modeling of milling process to predict surface roughness using artificial intelligent method

This thesis presents the milling process modeling to predict surface roughness. Proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop ma...

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Main Author:	Mohammad Rizal, Abdul Lani
Format:	Undergraduates Project Papers
Language:	English
Published:	2009
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/800/ http://umpir.ump.edu.my/id/eprint/800/ http://umpir.ump.edu.my/id/eprint/800/1/Mohammad_Rizal_Abdul_Lani.pdf

id	ump-800
recordtype	eprints
spelling	ump-8002015-03-03T07:46:57Z http://umpir.ump.edu.my/id/eprint/800/ Modeling of milling process to predict surface roughness using artificial intelligent method Mohammad Rizal, Abdul Lani TA Engineering (General). Civil engineering (General) This thesis presents the milling process modeling to predict surface roughness. Proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction. 2009-11 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/800/1/Mohammad_Rizal_Abdul_Lani.pdf Mohammad Rizal, Abdul Lani (2009) Modeling of milling process to predict surface roughness using artificial intelligent method. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:45888&theme=UMP2
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) Mohammad Rizal, Abdul Lani Modeling of milling process to predict surface roughness using artificial intelligent method
description	This thesis presents the milling process modeling to predict surface roughness. Proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction.
format	Undergraduates Project Papers
author	Mohammad Rizal, Abdul Lani
author_facet	Mohammad Rizal, Abdul Lani
author_sort	Mohammad Rizal, Abdul Lani
title	Modeling of milling process to predict surface roughness using artificial intelligent method
title_short	Modeling of milling process to predict surface roughness using artificial intelligent method
title_full	Modeling of milling process to predict surface roughness using artificial intelligent method
title_fullStr	Modeling of milling process to predict surface roughness using artificial intelligent method
title_full_unstemmed	Modeling of milling process to predict surface roughness using artificial intelligent method
title_sort	modeling of milling process to predict surface roughness using artificial intelligent method
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/800/ http://umpir.ump.edu.my/id/eprint/800/ http://umpir.ump.edu.my/id/eprint/800/1/Mohammad_Rizal_Abdul_Lani.pdf
first_indexed	2023-09-18T21:53:22Z
last_indexed	2023-09-18T21:53:22Z
_version_	1777413917073473536

Modeling of milling process to predict surface roughness using artificial intelligent method

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