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008 090721t2008 my a f m 001 0 eng|d
020 _aTHE0006504(Local)
039 9 _a201905141242
_bamirul
_c201804171459
_dhuda
_c201107132243
_dVLOAD
_c200908141650
_dVLOAD
_y200907211623
_zida84
040 _aUMP
090 _aTJ1185 .G36 2008 rs Bc.
100 3 _aGan, Sin Yi
245 1 2 _aMathematical modeling to predict surface roughness in milling process /
_cGan Sin Yi
246 3 _aMathematical modeling to predict surface roughness in milling process /
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2008
300 _a71 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2008
520 3 _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.
650 0 _aMilling (Metal-work)
650 0 _aMachining
_xMathematical models
999 _aVIRTUA40
_c1271
_d1277
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5200*6500*6501*9992