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Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network / Annie Liew Ann Nee

By:

Liew, Annie Ann Nee

Material type: Text

TextPublication details: Kuantan, Pahang : UMP, 2010Description: xii, 47 p. : ill. (some col.) ; 30 cm. + 1 computer discISBN:

THE0006584(Local)

Other title:

Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network [computer file]

Subject(s):

Dissertation note: Project paper (Bachelor of Mechanical Engineering with Manufacturing) -- Universiti Malaysia Pahang - 2010 Abstract: 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.

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Title notes ( 3 )

Holdings
Item type	Current library	Call number	Copy number	Status	Date due	Barcode
Final Year Report	UMPLIB PEKAN	CD 5032 \| TJ1191 .L54 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054034
Final Year Report	UMPLIB PEKAN	TJ1191 .L54 2010 rs Bc. (Browse shelf(Opens below))	1	Not for loan		0000054033

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.