02685nam a2200265 a 4500 vtls000052411 KUKTEM 20251114204445.0 110329t2010 my a f m 000 0 eng d THE0006584(Local) 201905161030 amirul 201110040955 Fida 201107140041 VLOAD 201103301414 ida 201103290916 ida UMP TJ1191 .L54 2010 rs Bc. Liew, Annie Ann Nee Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network / Annie Liew Ann Nee Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network [computer file] Kuantan, Pahang : UMP, 2010 xii, 47 p. : ill. (some col.) ; 30 cm. + 1 computer disc Project paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2010 Bibliography : p. 47 This 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. Electric metal-cutting Machining 0 0 0 1 20000 20000 2019-09-04 0 CD 5032 | TJ1191 .L54 2010 rs Bc. 0000054034 2019-09-04 00:00:00 1 2019-09-04 PSM 0 0 0 1 20000 20000 2019-09-04 0 TJ1191 .L54 2010 rs Bc. 0000054033 2019-09-04 00:00:00 1 2019-09-04 PSM VIRTUA40 2364 2370 VTLSSORT0080*0200*0400*0900*1000*2450*2460*2600*3000*5020*5040*5200*6500*6501*9992