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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| Format: | Undergraduates Project Papers |
| Language: | English |
| Published: |
2009
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/2370/ http://umpir.ump.edu.my/id/eprint/2370/1/ELIE_SEM_SEMILIN.PDF |
| Summary: | 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. |
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