Cover Image

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...

Full description

Saved in:
Bibliographic Details
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/2370/1/ELIE_SEM_SEMILIN.PDF
http://umpir.ump.edu.my/id/eprint/2370/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.ump.umpir.2370
record_format eprints
spelling my.ump.umpir.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.
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
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/2370/1/ELIE_SEM_SEMILIN.PDF
http://umpir.ump.edu.my/id/eprint/2370/
_version_ 1643664603015544832
score 13.211869

Similar Items