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Predicting surface roughness in turning operation using extreme learning machine

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dc.citedby2
dc.contributor.authorNooraziah A.en_US
dc.contributor.authorTiagrajah V.J.en_US
dc.contributor.authorid55263605500en_US
dc.contributor.authorid35198314400en_US
dc.date.accessioned2023-05-16T02:47:24Z
dc.date.available2023-05-16T02:47:24Z
dc.date.issued2014
dc.description.abstractPrediction 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.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.4028/www.scientific.net/AMM.554.431
dc.identifier.epage435
dc.identifier.scopus2-s2.0-84903549200
dc.identifier.spage431
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84903549200&doi=10.4028%2fwww.scientific.net%2fAMM.554.431&partnerID=40&md5=5bdd52f3d9a21ab5c289f4cf4ace7904
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22102
dc.identifier.volume554
dc.publisherTrans Tech Publications Ltden_US
dc.sourceScopus
dc.sourcetitleApplied Mechanics and Materials
dc.titlePredicting surface roughness in turning operation using extreme learning machineen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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