Mathematical Modeling to Predict Surface Roughness in CNC Milling

Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness p...

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Main Authors:	M. F. F., Ab Rashid, Gan, Sin Yi, Noryanti, Muhammad
Format:	Article
Language:	English
Published:	WASET 2009
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/5276/ http://umpir.ump.edu.my/id/eprint/5276/ http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF

id	ump-5276
recordtype	eprints
spelling	ump-52762018-02-02T03:09:40Z http://umpir.ump.edu.my/id/eprint/5276/ Mathematical Modeling to Predict Surface Roughness in CNC Milling M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad TJ Mechanical engineering and machinery Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set. WASET 2009-05 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF M. F. F., Ab Rashid and Gan, Sin Yi and Noryanti, Muhammad (2009) Mathematical Modeling to Predict Surface Roughness in CNC Milling. Proceedings of World Academy of Science, Engineering and Technology, 3 (5). pp. 393-396. ISSN 2070-3740 http://waset.org/publications/5381/mathematical-modeling-to-predict-surface-roughness-in-cnc-milling
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad Mathematical Modeling to Predict Surface Roughness in CNC Milling
description	Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
format	Article
author	M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad
author_facet	M. F. F., Ab Rashid Gan, Sin Yi Noryanti, Muhammad
author_sort	M. F. F., Ab Rashid
title	Mathematical Modeling to Predict Surface Roughness in CNC Milling
title_short	Mathematical Modeling to Predict Surface Roughness in CNC Milling
title_full	Mathematical Modeling to Predict Surface Roughness in CNC Milling
title_fullStr	Mathematical Modeling to Predict Surface Roughness in CNC Milling
title_full_unstemmed	Mathematical Modeling to Predict Surface Roughness in CNC Milling
title_sort	mathematical modeling to predict surface roughness in cnc milling
publisher	WASET
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/5276/ http://umpir.ump.edu.my/id/eprint/5276/ http://umpir.ump.edu.my/id/eprint/5276/1/WASET_Mathematical_Modeling_to_Predict_Surface_Roughness.PDF
first_indexed	2023-09-18T22:00:32Z
last_indexed	2023-09-18T22:00:32Z
_version_	1777414367729418240

Mathematical Modeling to Predict Surface Roughness in CNC Milling

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