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Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models

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Main Authors: R., Izamshah, M., Akmal, M., Halim, M. S., Kasim, R., Zamri, M. S., Yob, M. S., A. Aziz, R. S. A., Abdullah
Other Authors: izamshah@utem.edu.my
Format: Article
Language:English
Published: Universiti Malaysia Perlis (UniMAP) 2022
Subjects:
Artificial neural networks
Full-factorial design
Neuro-fuzzy inference system
WEDT
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75795
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spelling my.unimap-757952022-08-02T08:44:35Z Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models R., Izamshah M., Akmal R., Izamshah M., Halim M. S., Kasim R., Zamri M. S., Yob M. S., A. Aziz R. S. A., Abdullah izamshah@utem.edu.my Advanced Manufacturing Centre (AMC), Universiti Teknikal Malaysia Melaka (UTeM) Fakulti Kejuruteraan Pembuatan, Universiti Teknikal Malaysia Melaka (UTeM) School of Information Technology and Electrical Engineering, The University of Queensland Artificial neural networks Full-factorial design Neuro-fuzzy inference system WEDT Link to publisher's homepage at http://ijneam.unimap.edu.my This work intended to assess the prediction and simulation effectiveness of the artificial neural network (ANN) with adaptive neuro-fuzzy inference system (ANFIS) approaches for modeling the material removal rate (MRR) in wire electrical discharge turning for fabrication of micro-pin made by Ti6Al4V. 16 experiments have been conducted according to full factorial design by varying four different WEDT input attributes namely pulse intensity, voltage open, wire tension and spindle speed. This dataset is aimed to be used for training and then, five more trials with random selection of input attributes is conducted to be established as the validation data. In developing the ANN model, Levenberg–Marquardt backpropagation training algorithm with ten neurons of hidden layer is employed and the Gaussian curve built-in membership function is used for developing the ANFIS model. The ANN and ANFIS model have been compared with experimental results. Both models indicated good predictions, however, the comparison revealed that the ANFIS model produced the closest result with the experiment compare than ANN. 2022 2022-08-02T08:44:35Z 2022-08-02T08:44:35Z 2022-03 Article International Journal of Nanoelectronics and Materials, vol.15 (Special Issue), 2022, pages 101-112 1997-4434 (Online) 1985-5761 (Printed) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75795 http://ijneam.unimap.edu.my en Special Issue ISSTE 2022; Universiti Malaysia Perlis (UniMAP)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Artificial neural networks
Full-factorial design
Neuro-fuzzy inference system
WEDT
spellingShingle Artificial neural networks
Full-factorial design
Neuro-fuzzy inference system
WEDT
R., Izamshah
M., Akmal
R., Izamshah
M., Halim
M. S., Kasim
R., Zamri
M. S., Yob
M. S., A. Aziz
R. S. A., Abdullah
Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
description Link to publisher's homepage at http://ijneam.unimap.edu.my
author2 izamshah@utem.edu.my
author_facet izamshah@utem.edu.my
R., Izamshah
M., Akmal
R., Izamshah
M., Halim
M. S., Kasim
R., Zamri
M. S., Yob
M. S., A. Aziz
R. S. A., Abdullah
format Article
author R., Izamshah
M., Akmal
R., Izamshah
M., Halim
M. S., Kasim
R., Zamri
M. S., Yob
M. S., A. Aziz
R. S. A., Abdullah
author_sort R., Izamshah
title Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
title_short Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
title_full Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
title_fullStr Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
title_full_unstemmed Prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
title_sort prediction of material removal rate in wire electrical discharge turning using artificial neural networks and adaptive neuro-fuzzy models
publisher Universiti Malaysia Perlis (UniMAP)
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/75795
_version_ 1743108376065212416
score 13.222552

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