02393nam a2200217 a 4500001001400000003000700014005001700021008004100038020002200079040000800101100002400109245014800133246014300281260003400424300006400458502011100522504002500633520147600658650002702134650001402161vtls000052411KUKTEM20251114204445.0110329t2010    my a   f m    000 0 eng d  aTHE0006584(Local)  aUMP1 aLiew, Annie Ann Nee10aOptimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network /cAnnie Liew Ann Nee3 aOptimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural networkh[computer file]  aKuantan, Pahang :bUMP,c2010  axii, 47 p. :bill. (some col.) ;c30 cm. +e1 computer disc  aProject paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2010  aBibliography : p. 473 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. 0aElectric metal-cutting 0aMachining