Publication: Predicting surface roughness in turning operation using extreme learning machine
dc.citedby | 2 | |
dc.contributor.author | Nooraziah A. | en_US |
dc.contributor.author | Tiagrajah V.J. | en_US |
dc.contributor.authorid | 55263605500 | en_US |
dc.contributor.authorid | 35198314400 | en_US |
dc.date.accessioned | 2023-05-16T02:47:24Z | |
dc.date.available | 2023-05-16T02:47:24Z | |
dc.date.issued | 2014 | |
dc.description.abstract | 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. | en_US |
dc.description.nature | Final | en_US |
dc.identifier.doi | 10.4028/www.scientific.net/AMM.554.431 | |
dc.identifier.epage | 435 | |
dc.identifier.scopus | 2-s2.0-84903549200 | |
dc.identifier.spage | 431 | |
dc.identifier.uri | 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 | |
dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/22102 | |
dc.identifier.volume | 554 | |
dc.publisher | Trans Tech Publications Ltd | en_US |
dc.source | Scopus | |
dc.sourcetitle | Applied Mechanics and Materials | |
dc.title | Predicting surface roughness in turning operation using extreme learning machine | en_US |
dc.type | Conference Paper | en_US |
dspace.entity.type | Publication |