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Multi objective machining estimation model using orthogonal and neural network

Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi ort...

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主要な著者: Yusoff, Y., Zain, A. M., Sharif, S., Sallehuddin, R.
フォーマット: 論文
言語:English
出版事項: Penerbit UTM Press 2016
主題:
TJ Mechanical engineering and machinery
オンライン･アクセス:http://eprints.utm.my/id/eprint/70023/1/YuslizaYusoff2016_MultiObjectiveMachiningEstimationModel.pdf
http://eprints.utm.my/id/eprint/70023/
http://dx.doi.org/10.11113/jt.v78.10116
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spelling my.utm.700232017-11-14T06:23:15Z http://eprints.utm.my/id/eprint/70023/ Multi objective machining estimation model using orthogonal and neural network Yusoff, Y. Zain, A. M. Sharif, S. Sallehuddin, R. TJ Mechanical engineering and machinery Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems. Penerbit UTM Press 2016 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/70023/1/YuslizaYusoff2016_MultiObjectiveMachiningEstimationModel.pdf Yusoff, Y. and Zain, A. M. and Sharif, S. and Sallehuddin, R. (2016) Multi objective machining estimation model using orthogonal and neural network. Jurnal Teknologi, 78 (12-2). pp. 11-18. ISSN 0127-9696 http://dx.doi.org/10.11113/jt.v78.10116 DOI:10.11113/jt.v78.10116
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TJ Mechanical engineering and machinery
spellingShingle TJ Mechanical engineering and machinery
Yusoff, Y.
Zain, A. M.
Sharif, S.
Sallehuddin, R.
Multi objective machining estimation model using orthogonal and neural network
description Much hard work has been done to model the machining operations using the neural network (NN). However, the selection of suitable neural network model in machining optimization area especially in multi objective area is unsupervised and resulted in pointless trials. Thus, a combination of Taguchi orthogonal and NN modeling approach is tested on two types of electrical discharge machining (EDM) operations; Cobalt Bonded Tungsten Carbide (WC-Co) and Inconel 718 to observe the efficiency of proposed approach on different numbers of objectives. WC-Co EDM considered two objective functions and Inconel 718 EDM considered four objective functions. It is found that one hidden layer 4-8-2 layer recurrent neural network (LRNN) is the best estimation model for WC-Co machining and one hidden layer 5-14-4 cascade feed forward back propagation (CFBP) is the best estimation model for Inconel 718 EDM. The results are compared with trial-error approach and it is proven that the proposed modeling approach is able to improve the machining performances and works efficiently on two-objective problems.
format Article
author Yusoff, Y.
Zain, A. M.
Sharif, S.
Sallehuddin, R.
author_facet Yusoff, Y.
Zain, A. M.
Sharif, S.
Sallehuddin, R.
author_sort Yusoff, Y.
title Multi objective machining estimation model using orthogonal and neural network
title_short Multi objective machining estimation model using orthogonal and neural network
title_full Multi objective machining estimation model using orthogonal and neural network
title_fullStr Multi objective machining estimation model using orthogonal and neural network
title_full_unstemmed Multi objective machining estimation model using orthogonal and neural network
title_sort multi objective machining estimation model using orthogonal and neural network
publisher Penerbit UTM Press
publishDate 2016
url http://eprints.utm.my/id/eprint/70023/1/YuslizaYusoff2016_MultiObjectiveMachiningEstimationModel.pdf
http://eprints.utm.my/id/eprint/70023/
http://dx.doi.org/10.11113/jt.v78.10116
_version_ 1643656076161187840
score 13.251813

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