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020 _aTHE0006584(Local)
039 9 _a201905161030
_bamirul
_c201110040955
_dFida
_c201107140041
_dVLOAD
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_zida
040 _aUMP
090 _aTJ1191 .L54 2010 rs Bc.
100 1 _aLiew, Annie Ann Nee
245 1 0 _aOptimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network /
_cAnnie Liew Ann Nee
246 3 _aOptimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
_h[computer file]
260 _aKuantan, Pahang :
_bUMP,
_c2010
300 _axii, 47 p. :
_bill. (some col.) ;
_c30 cm. +
_e1 computer disc
502 _aProject paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2010
504 _aBibliography : p. 47
520 3 _aThis report presents the artificial neural network model to predict the optimal machining parameters for Ti-6Al-4V through electrical discharge machining (EDM) using copper as an electrode and positive polarity of the electrode. The objective of this paper is to investigate how the peak current, servor voltage, pulse on- and off-time in EDM effect on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFN) is used to develop the Artificial Neural Network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method and response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out by doing confirmation test. The optimum machining conditions are estimated and verified with proposed ANN model. It is observed that the developed model is within the limits of the agreeable error with experimental results. Sensitivity analysis is carried out to investigate the relative influence of factors on the performance measures. It is observed that peak current effectively influences the performance measures. The reported results indicate that the proposed ANN models can satisfactorily evaluate the MRR, TWR as well as SR in EDM. Therefore, the proposed model can be considered as valuable tools for the process planning for EDM and leads to economical industrial machining by optimizing the input parameters.
650 0 _aElectric metal-cutting
650 0 _aMachining
999 _aVIRTUA40
_c2364
_d2370
999 _aVTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*9992