Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys

As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguch...

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Main Authors: Yusoff, Yusliza, Mohd. Zain, Azlan, Amrin, Astuty, Sharif, Safian, Haron, Habibollah, Sallehuddin, Roselina
Format: Article
Published: Springer Netherlands 2019
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Online Access:http://eprints.utm.my/id/eprint/87662/
http://dx.doi.org/10.1007/s10462-017-9602-2
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Institution: Universiti Teknologi Malaysia
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spelling my.utm.876622020-11-30T12:57:39Z http://eprints.utm.my/id/eprint/87662/ Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys Yusoff, Yusliza Mohd. Zain, Azlan Amrin, Astuty Sharif, Safian Haron, Habibollah Sallehuddin, Roselina QA75 Electronic computers. Computer science As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguchi) is employed in the procedure of network function and network architecture assortment to avoid excessive random trial experimentations. This proposed orthogonal based ANN modelling is employed on WEDM of Ti–48Al intermetallic alloys. Meanwhile modified multi objective genetic algorithm (multiGA) is used as the optimization technique. Material removal rate (MRR), surface roughness (Ra), cutting speed (Vc) and width of kerf (Dk) are the machining performances considered in this study. Five machining parameters observed from the previous researches are chosen as significant factors to the machining performances in this study, which are pulse on time, pulse off time, peak current, feed rate and servo voltage. Experimental studies are carried out to verify the machining performances suggested by this approach. Feed forward back propagation neural network (FFNN) is found to be the best network type on the selected dataset. Two hidden layer 5–6–6–4 FFNN showed the most precise and generalized network architecture with very good prediction accuracy. The proposed approach, OrthoANN, reduced ANN experimentation time by a large scale and produced viable results for machining optimization when integrated with multiGA. Springer Netherlands 2019-06-01 Article PeerReviewed Yusoff, Yusliza and Mohd. Zain, Azlan and Amrin, Astuty and Sharif, Safian and Haron, Habibollah and Sallehuddin, Roselina (2019) Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys. Artificial Intelligence Review, 52 (1). pp. 671-706. ISSN 0269-2821 http://dx.doi.org/10.1007/s10462-017-9602-2 DOI:10.1007/s10462-017-9602-2
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/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Yusoff, Yusliza
Mohd. Zain, Azlan
Amrin, Astuty
Sharif, Safian
Haron, Habibollah
Sallehuddin, Roselina
Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
description As surveyed, many efforts have been made to model the performances of electrical discharge machining (EDM) using artificial neural network (ANN). However, the selections of the network parameters were mostly prepared in a random manner, resulting to unnecessary trials. Thus, orthogonal array (Taguchi) is employed in the procedure of network function and network architecture assortment to avoid excessive random trial experimentations. This proposed orthogonal based ANN modelling is employed on WEDM of Ti–48Al intermetallic alloys. Meanwhile modified multi objective genetic algorithm (multiGA) is used as the optimization technique. Material removal rate (MRR), surface roughness (Ra), cutting speed (Vc) and width of kerf (Dk) are the machining performances considered in this study. Five machining parameters observed from the previous researches are chosen as significant factors to the machining performances in this study, which are pulse on time, pulse off time, peak current, feed rate and servo voltage. Experimental studies are carried out to verify the machining performances suggested by this approach. Feed forward back propagation neural network (FFNN) is found to be the best network type on the selected dataset. Two hidden layer 5–6–6–4 FFNN showed the most precise and generalized network architecture with very good prediction accuracy. The proposed approach, OrthoANN, reduced ANN experimentation time by a large scale and produced viable results for machining optimization when integrated with multiGA.
format Article
author Yusoff, Yusliza
Mohd. Zain, Azlan
Amrin, Astuty
Sharif, Safian
Haron, Habibollah
Sallehuddin, Roselina
author_facet Yusoff, Yusliza
Mohd. Zain, Azlan
Amrin, Astuty
Sharif, Safian
Haron, Habibollah
Sallehuddin, Roselina
author_sort Yusoff, Yusliza
title Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
title_short Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
title_full Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
title_fullStr Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
title_full_unstemmed Orthogonal based ANN and multiGA for optimization on WEDM of Ti–48Al intermetallic alloys
title_sort orthogonal based ann and multiga for optimization on wedm of ti–48al intermetallic alloys
publisher Springer Netherlands
publishDate 2019
url http://eprints.utm.my/id/eprint/87662/
http://dx.doi.org/10.1007/s10462-017-9602-2
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