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008 100524t2009 my dao f m 000 0 eng d
020 _aTHE0005782(Local)
039 9 _a201905101616
_baida
_c201107132321
_dVLOAD
_c201103301003
_dida
_y201005241258
_zida
040 _aUMP
090 _aTA418.7 .R59 2009 rs Bc.
100 0 _aMohammad Rizal Abdul Lani
245 1 0 _aModeling of milling process to predict surface roughness using artificial intelligent method /
_cMohammad Rizal Bin Abdul Lani
246 3 _aModeling of milling process to predict surface roughness using artificial intelligent method
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2009
300 _axiii, 64 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009
504 _aBibliography : p. 60-61
520 3 _aThis thesis presents the milling process modeling to predict surface roughness. Proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction.
650 0 _aSurface roughness
650 0 _aMachining
650 0 _aArtificial intelligence
999 _aVIRTUA40
_c1910
_d1916
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992