02021nam a2200205 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100001600109245008800125246009900213260003400312300005900346502011100405520123900516650002501755650003501780vtls000040641KUKTEM20251114204407.0090721t2008    my a   f m    001 0 eng|d  aTHE0006504(Local)  aUMP3 aGan, Sin Yi12aMathematical modeling to predict surface roughness in milling process /cGan Sin Yi3 aMathematical modeling to predict surface roughness in milling process /h[electronic resource]  aKuantan, Pahang :bUMP,c2008  a71 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 20083 aSurface 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. 0aMilling (Metal-work) 0aMachiningxMathematical models