02312nam a2200253 a 4500 vtls000040641 KUKTEM 20251114204407.0 090721t2008 my a f m 001 0 eng|d THE0006504(Local) 201905141242 amirul 201804171459 huda 201107132243 VLOAD 200908141650 VLOAD 200907211623 ida84 UMP TJ1185 .G36 2008 rs Bc. Gan, Sin Yi Mathematical modeling to predict surface roughness in milling process / Gan Sin Yi Mathematical modeling to predict surface roughness in milling process / [electronic resource] Kuantan, Pahang : UMP, 2008 71 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2008 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. Milling (Metal-work) Machining Mathematical models 0 0 0 1 20000 20000 2019-09-04 0 TJ1185 .G36 2008 rs Bc. 0000038059 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 CD 3383 | TJ1185 .G36 2008 rs Bc. 0000038060 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 1271 1277 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992