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003 KUKTEM
005 20251114204408.0
008 100504t2009 my a f m 000 0 eng d
020 _aTHE0006615(Local)
039 9 _a201905161253
_bamirul
_c201107132203
_dVLOAD
_y201005041335
_zida
040 _aUMP
090 _aTJ1225 .Z36 2009 rs Bc.
100 0 _aMohd Zamri Ja'afar
245 1 0 _aDevelop 1st and 2nd mathematical model for torque prediction by using response surface methodology when milling modified AISI P20 tool steel /
_cMohd Zamri Bin Ja'afar
246 3 _aDevelop 1st and 2nd mathematical model for torque prediction by using response surface methodology when milling modified AISI P20 tool steel
_h[electronic resource]
260 _aKuantan, Pahang :
_bUMP,
_c2009
300 _axv, 49 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. 43-44
520 3 _aThe present paper discusses the development of the first and second order models for predicting the cutting torque produced in end-milling operation of modified AISI P20 tool steel. The first and second order cutting torque equations are developed using the response surface methodology (RSM) to study the effect of four input cutting parameters which is cutting speed, feed rate, radial depth and axial depth of cut on cutting power. The cutting torque contours with respect to input parameters are presented and the predictive models analyses are performed with the aid of the statistical software package Minitab. The separate affect of individual input factors and the interaction between these factors are also investigated in this study. In first order model, the increase in the cutting speed, feed rate, axial and radial depths of cut will cause the cutting torque to become larger. The received second order equation shows, based on the variance analysis, that the cutting torque decreased when cutting speed, federate, axial and radial depth of cut is reduced. The predictive models in this study are believed to produce values of the longitudinal component of the cutting torque close to those readings recorded experimentally with a 95% confident interval.
650 0 _aMilling-machines
650 0 _aTorque
_xMathematical models
650 0 _aResponse surfaces (Statistics)
999 _aVIRTUA40
_c1308
_d1314
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*6502*9992