Predicting surface roughness in turning operation using extreme learning machine

Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the al...

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Main Authors: Nooraziah A., Tiagrajah V.J.
Other Authors: 55263605500
Format: Conference Paper
Published: Trans Tech Publications Ltd 2023
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Institution: Universiti Tenaga Nasional
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spelling my.uniten.dspace-221022023-05-16T10:47:24Z Predicting surface roughness in turning operation using extreme learning machine Nooraziah A. Tiagrajah V.J. 55263605500 35198314400 Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection. © (2014) Trans Tech Publications, Switzerland. Final 2023-05-16T02:47:24Z 2023-05-16T02:47:24Z 2014 Conference Paper 10.4028/www.scientific.net/AMM.554.431 2-s2.0-84903549200 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903549200&doi=10.4028%2fwww.scientific.net%2fAMM.554.431&partnerID=40&md5=5bdd52f3d9a21ab5c289f4cf4ace7904 https://irepository.uniten.edu.my/handle/123456789/22102 554 431 435 Trans Tech Publications Ltd Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection. © (2014) Trans Tech Publications, Switzerland.
author2 55263605500
author_facet 55263605500
Nooraziah A.
Tiagrajah V.J.
format Conference Paper
author Nooraziah A.
Tiagrajah V.J.
spellingShingle Nooraziah A.
Tiagrajah V.J.
Predicting surface roughness in turning operation using extreme learning machine
author_sort Nooraziah A.
title Predicting surface roughness in turning operation using extreme learning machine
title_short Predicting surface roughness in turning operation using extreme learning machine
title_full Predicting surface roughness in turning operation using extreme learning machine
title_fullStr Predicting surface roughness in turning operation using extreme learning machine
title_full_unstemmed Predicting surface roughness in turning operation using extreme learning machine
title_sort predicting surface roughness in turning operation using extreme learning machine
publisher Trans Tech Publications Ltd
publishDate 2023
_version_ 1806424556425969664