Investigation and modelling prediction on surface roughness of titanium in dry turning operation

Surface roughness basically known as Ra is one of the best important requirements in machining process. Titanium generally used for part requiring greatest reliability, therefore surface integrity must be maintained. The proper setting of cutting parameter is crucial before process take place in...

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Main Author:	Elie Sem, Semilin
Format:	Undergraduates Project Papers
Language:	English
Published:	2009
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/2370/ http://umpir.ump.edu.my/id/eprint/2370/1/ELIE_SEM_SEMILIN.PDF

id	ump-2370
recordtype	eprints
spelling	ump-23702015-03-03T07:56:15Z http://umpir.ump.edu.my/id/eprint/2370/ Investigation and modelling prediction on surface roughness of titanium in dry turning operation Elie Sem, Semilin TJ Mechanical engineering and machinery Surface roughness basically known as Ra is one of the best important requirements in machining process. Titanium generally used for part requiring greatest reliability, therefore surface integrity must be maintained. The proper setting of cutting parameter is crucial before process take place in order to get the better surface finish. This research presents the development of mathematical model for surface roughness prediction before turning process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 9 samples were run in this study by using CNC lathe Machine with non-coolant cutting condition. Multiple Regression Methods used to determine the correlation between the criterion variable and the combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using multiple regression method, the average percentage deviation of the Non-Adding Interaction term model was 22394% which shows the statistical model for Non-Adding Interaction term model could predict 74.406% accuracy. The Adding Interaction term model shows improvement in accuracy of the statistical model. It shows the statistical model could predict 86.57% accuracy for surface roughness which means 13.43% was the percentage deviation on model of surface roughness. Analysis of Variance (ANOVA) shows at least one of the population regression coefficients was not equal to zero for Non-Adding interaction Term model and the population regression coefficient didn't significantly differ from zero for Adding Interaction term model. 2009-11 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/2370/1/ELIE_SEM_SEMILIN.PDF Elie Sem, Semilin (2009) Investigation and modelling prediction on surface roughness of titanium in dry turning operation. 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	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Elie Sem, Semilin Investigation and modelling prediction on surface roughness of titanium in dry turning operation
description	Surface roughness basically known as Ra is one of the best important requirements in machining process. Titanium generally used for part requiring greatest reliability, therefore surface integrity must be maintained. The proper setting of cutting parameter is crucial before process take place in order to get the better surface finish. This research presents the development of mathematical model for surface roughness prediction before turning process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 9 samples were run in this study by using CNC lathe Machine with non-coolant cutting condition. Multiple Regression Methods used to determine the correlation between the criterion variable and the combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using multiple regression method, the average percentage deviation of the Non-Adding Interaction term model was 22394% which shows the statistical model for Non-Adding Interaction term model could predict 74.406% accuracy. The Adding Interaction term model shows improvement in accuracy of the statistical model. It shows the statistical model could predict 86.57% accuracy for surface roughness which means 13.43% was the percentage deviation on model of surface roughness. Analysis of Variance (ANOVA) shows at least one of the population regression coefficients was not equal to zero for Non-Adding interaction Term model and the population regression coefficient didn't significantly differ from zero for Adding Interaction term model.
format	Undergraduates Project Papers
author	Elie Sem, Semilin
author_facet	Elie Sem, Semilin
author_sort	Elie Sem, Semilin
title	Investigation and modelling prediction on surface roughness of titanium in dry turning operation
title_short	Investigation and modelling prediction on surface roughness of titanium in dry turning operation
title_full	Investigation and modelling prediction on surface roughness of titanium in dry turning operation
title_fullStr	Investigation and modelling prediction on surface roughness of titanium in dry turning operation
title_full_unstemmed	Investigation and modelling prediction on surface roughness of titanium in dry turning operation
title_sort	investigation and modelling prediction on surface roughness of titanium in dry turning operation
publishDate	2009
url	http://umpir.ump.edu.my/id/eprint/2370/ http://umpir.ump.edu.my/id/eprint/2370/1/ELIE_SEM_SEMILIN.PDF
first_indexed	2023-09-18T21:56:07Z
last_indexed	2023-09-18T21:56:07Z
_version_	1777414090462855168

Investigation and modelling prediction on surface roughness of titanium in dry turning operation

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