TY - JFULL
AU - Ab. Rashid M.F.F. and Gan S.Y. and Muhammad N.Y.
PY - 2009/6/
TI - Mathematical Modeling to Predict Surface Roughness in CNC Milling
T2 - International Journal of Mechanical and Mechatronics Engineering
SP - 535
EP - 539
VL - 3
SN - 1307-6892
UR - https://publications.waset.org/pdf/5381
PU - World Academy of Science, Engineering and Technology
NX - Open Science Index 29, 2009
N2 - Surface roughness (Ra) is one of the most important requirements in machining process. In order to obtain better surface roughness, the proper setting of cutting parameters is crucial before the process take place. This research presents the development of mathematical model for surface roughness prediction before milling process in order to evaluate the fitness of machining parameters; spindle speed, feed rate and depth of cut. 84 samples were run in this study by using FANUC CNC Milling α-Τ14ιE. Those samples were randomly divided into two data sets- the training sets (m=60) and testing sets(m=24). ANOVA analysis showed that at least one of the population regression coefficients was not zero. Multiple Regression Method was used to determine the correlation between a criterion variable and a combination of predictor variables. It was established that the surface roughness is most influenced by the feed rate. By using Multiple Regression Method equation, the average percentage deviation of the testing set was 9.8% and 9.7% for training data set. This showed that the statistical model could predict the surface roughness with about 90.2% accuracy of the testing data set and 90.3% accuracy of the training data set.
ER -