Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network

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, ser...

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Main Author:	Liew, Annie Ann Nee
Format:	Undergraduates Project Papers
Language:	English
Published:	2010
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/1403/ http://umpir.ump.edu.my/id/eprint/1403/ http://umpir.ump.edu.my/id/eprint/1403/1/7._Liew%2C_Annie_Ann_Nee_%28_CD_5032%29.pdf

id	ump-1403
recordtype	eprints
spelling	ump-14032015-03-03T07:50:46Z http://umpir.ump.edu.my/id/eprint/1403/ Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network Liew, Annie Ann Nee TJ Mechanical engineering and machinery 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. 2010-12 Undergraduates Project Papers NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/1403/1/7._Liew%2C_Annie_Ann_Nee_%28_CD_5032%29.pdf Liew, Annie Ann Nee (2010) Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network. Faculty of Mechanical Engineering, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:52411&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TJ Mechanical engineering and machinery
spellingShingle	TJ Mechanical engineering and machinery Liew, Annie Ann Nee Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
description	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.
format	Undergraduates Project Papers
author	Liew, Annie Ann Nee
author_facet	Liew, Annie Ann Nee
author_sort	Liew, Annie Ann Nee
title	Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
title_short	Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
title_full	Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
title_fullStr	Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
title_full_unstemmed	Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
title_sort	optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network
publishDate	2010
url	http://umpir.ump.edu.my/id/eprint/1403/ http://umpir.ump.edu.my/id/eprint/1403/ http://umpir.ump.edu.my/id/eprint/1403/1/7._Liew%2C_Annie_Ann_Nee_%28_CD_5032%29.pdf
first_indexed	2023-09-18T21:54:31Z
last_indexed	2023-09-18T21:54:31Z
_version_	1777413989147344896

Optimization of machining parameters of titanium alloy in electric discharge machining based on artificial neural network

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