Investigation and modelling prediction on surface roughness of titanium in dry turning operation - Elie Sem, Semilin (2009)
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.