Publication:
An Optimal-Pruned Extreme Learning Machine based modelling of surface roughness

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dc.citedby2
dc.contributor.authorJanahiraman T.V.en_US
dc.contributor.authorAhmad N.en_US
dc.contributor.authorid35198314400en_US
dc.contributor.authorid56486827000en_US
dc.date.accessioned2023-05-29T06:00:33Z
dc.date.available2023-05-29T06:00:33Z
dc.date.issued2015
dc.descriptionBackpropagation; Computer control systems; Cutting tools; Forecasting; Knowledge acquisition; Learning systems; Neural networks; Numerical methods; Surface properties; Turning; Back propagation neural networks; Computer numerical control; Cutting operations; Efficient predictions; Extreme learning machine; Response surface method; Response surface methodology; Surface roughness model; Surface roughnessen_US
dc.description.abstractA computer based modelling and prediction method is vital in the field of Computer Numerical Control based cutting operation. The final quality of finished surface is mainly influenced by the interaction between the work piece, cutting tool and machining system. Therefore, many researchers attempted to develop an efficient prediction systems for surface roughness before machining. In this paper, Optimal Pruned Extreme Learning Machine (OPELM) is proposed for modelling and predicting surface roughness with respect to its cutting parameters in turning based machining process. The surface roughness models obtained from other methods such as Response Surface Method, Neural Network and Extreme Learning Machine were compared with the experimental results. Our experimental study consist of 15 workpieces that were used for cutting using turning operation. The correlation between the input parameters such as feed rate, cutting speed and depth of cut with surface roughness was modelled using OPELM. Based on our study, OPELM performed the best in modelling and predicting based on unknown set of input. � 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7066644
dc.identifier.doi10.1109/ICIMU.2014.7066644
dc.identifier.epage280
dc.identifier.scopus2-s2.0-84937510545
dc.identifier.spage276
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937510545&doi=10.1109%2fICIMU.2014.7066644&partnerID=40&md5=f2e010bdc9af5b2f9e942aa494b978a6
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22372
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleConference Proceedings - 6th International Conference on Information Technology and Multimedia at UNITEN: Cultivating Creativity and Enabling Technology Through the Internet of Things, ICIMU 2014
dc.titleAn Optimal-Pruned Extreme Learning Machine based modelling of surface roughnessen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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