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- W6854311 abstract "Surface roughness basically known as Ra is one of the best important requirementsin machining process. Titanium generally used for part requiring greatest reliability,therefore surface integrity must be maintained. The proper setting of cutting parameter iscrucial before process take place in order to get the better surface finish. This researchpresents the development of mathematical model for surface roughness prediction beforeturning process in order to evaluate the fitness of machining parameters; spindle speed, feedrate and depth of cut. 9 samples were run in this study by using CNC lathe Machine withnon-coolant cutting condition. Multiple Regression Methods used to determine thecorrelation between the criterion variable and the combination of predictor variables. It wasestablished that the surface roughness is most influenced by the feed rate. By using multipleregression method, the average percentage deviation of the Non-Adding Interaction termmodel was 22394% which shows the statistical model for Non-Adding Interaction termmodel could predict 74.406% accuracy. The Adding Interaction term model showsimprovement in accuracy of the statistical model. It shows the statistical model couldpredict 86.57% accuracy for surface roughness which means 13.43% was the percentagedeviation on model of surface roughness. Analysis of Variance (ANOVA) shows at leastone of the population regression coefficients was not equal to zero for Non-Addinginteraction Term model and the population regression coefficient didn't significantly differfrom zero for Adding Interaction term model." @default.
- W6854311 created "2016-06-24" @default.
- W6854311 creator A5062777850 @default.
- W6854311 date "2009-11-01" @default.
- W6854311 modified "2023-09-27" @default.
- W6854311 title "Investigation and modelling prediction on surface roughness of titanium in dry turning operation" @default.
- W6854311 hasPublicationYear "2009" @default.
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