Publication:
Modeling of Surface Roughness in Turning Operation Using Extreme Learning Machine

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dc.citedby9
dc.contributor.authorAhmad N.en_US
dc.contributor.authorJanahiraman T.V.en_US
dc.contributor.authorTarlochan F.en_US
dc.contributor.authorid56486827000en_US
dc.contributor.authorid35198314400en_US
dc.contributor.authorid9045273600en_US
dc.date.accessioned2023-05-29T06:00:51Z
dc.date.available2023-05-29T06:00:51Z
dc.date.issued2015
dc.description.abstractPrediction model allows the machinist to determine the values of the cutting performance before machining. According to the literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Response surface methodology (RSM) is a statistical method that only predicts effectively within the observed data provided. Most artificial intelligent systems mostly had an issue with user-defined data and long processing time. Recently, the extreme learning machine (ELM) method has been introduced, combining the single hidden layer feed- forward neural network with analytically determined output weights. The advantage of this method is that it can overcome the limitations due to the previous methods which include too many engineers� judgment and slow iterative learning phase. Therefore, in this study, the ELM was proposed to model the surface roughness based on RSM design of experiment. The results indicate that ELM can yield satisfactory solution for predicting the response within a few seconds and with small amount of error. � 2014, King Fahd University of Petroleum and Minerals.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1007/s13369-014-1420-0
dc.identifier.epage602
dc.identifier.issue2
dc.identifier.scopus2-s2.0-84921350919
dc.identifier.spage595
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84921350919&doi=10.1007%2fs13369-014-1420-0&partnerID=40&md5=d8f7193afe7fb4fa565e30cac3a5de35
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22416
dc.identifier.volume40
dc.publisherSpringer Verlagen_US
dc.sourceScopus
dc.sourcetitleArabian Journal for Science and Engineering
dc.titleModeling of Surface Roughness in Turning Operation Using Extreme Learning Machineen_US
dc.typeArticleen_US
dspace.entity.typePublication
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