Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach

This paper presents the artificial intelligence 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 the peak current, servo volt...

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Main Author:	M. M., Rahman
Format:	Article
Language:	English
Published:	Academic Journals 2012
Subjects:	TJ Mechanical engineering and machinery
Online Access:	http://umpir.ump.edu.my/id/eprint/6844/ http://umpir.ump.edu.my/id/eprint/6844/ http://umpir.ump.edu.my/id/eprint/6844/1/Modelling_of_Machining_Parameters_of_Ti-64I-4V_for_electric_discharge_machining.pdf

id	ump-6844
recordtype	eprints
spelling	ump-68442018-01-25T03:13:33Z http://umpir.ump.edu.my/id/eprint/6844/ Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach M. M., Rahman TJ Mechanical engineering and machinery This paper presents the artificial intelligence 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 the peak current, servo voltage, pulse on- and pulse off-time in EDM effects on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFNN) is used to develop the artificial neural network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method by using response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through analysis of variance (ANOVA). 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. Academic Journals 2012-02 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/6844/1/Modelling_of_Machining_Parameters_of_Ti-64I-4V_for_electric_discharge_machining.pdf M. M., Rahman (2012) Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach. Scientific Research and Essay , 7 (8). pp. 881-890. ISSN 1992-2248 DOI: 10.5897/SRE10.1116
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 M. M., Rahman Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
description	This paper presents the artificial intelligence 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 the peak current, servo voltage, pulse on- and pulse off-time in EDM effects on material removal rate (MRR), tool wear rate (TWR) and surface roughness (SR). Radial basis function neural network (RBFNN) is used to develop the artificial neural network (ANN) modeling of MRR, TWR and SR. Design of experiments (DOE) method by using response surface methodology (RSM) techniques are implemented. The validity test of the fit and adequacy of the proposed models has been carried out through analysis of variance (ANOVA). 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	Article
author	M. M., Rahman
author_facet	M. M., Rahman
author_sort	M. M., Rahman
title	Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
title_short	Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
title_full	Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
title_fullStr	Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
title_full_unstemmed	Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach
title_sort	modeling of machining parameters of ti-6al-4v for electric discharge machining: a neural network approach
publisher	Academic Journals
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/6844/ http://umpir.ump.edu.my/id/eprint/6844/ http://umpir.ump.edu.my/id/eprint/6844/1/Modelling_of_Machining_Parameters_of_Ti-64I-4V_for_electric_discharge_machining.pdf
first_indexed	2023-09-18T22:02:59Z
last_indexed	2023-09-18T22:02:59Z
_version_	1777414522041008128

Modeling of Machining Parameters of Ti-6Al-4V for Electric Discharge Machining: A Neural Network Approach

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