Publication:
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation

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dc.citedby7
dc.contributor.authorJanahiraman T.V.en_US
dc.contributor.authorAhmad N.en_US
dc.contributor.authorNordin F.H.en_US
dc.contributor.authorid35198314400en_US
dc.contributor.authorid56486827000en_US
dc.contributor.authorid25930510500en_US
dc.date.accessioned2023-05-29T06:52:21Z
dc.date.available2023-05-29T06:52:21Z
dc.date.issued2018
dc.descriptionComputer control systems; Knowledge acquisition; Learning systems; Manufacture; Particle swarm optimization (PSO); Turning; Artificial intelligent techniques; Extreme learning machine; In-process parameters; Modelling techniques; Optimal cutting parameters; Optimization techniques; Performance functions; Traditional techniques; Swarm intelligenceen_US
dc.description.abstractThe CNC machine is controlled by manipulating cutting parameters that could directly influence the process performance. Many optimization methods has been applied to obtain the optimal cutting parameters for the desired performance function. Nonetheless, the industry still uses the traditional technique to obtain those values. Lack of knowledge on optimization techniques is the main reason for this issue to be prolonged. Therefore, the simple yet easy to implement, Optimal Cutting Parameters Selection System is introduced to help the manufacturer to easily understand and determine the best optimal parameters for their turning operation. This new system consists of two stages which are modelling and optimization. In modelling of input-output and in-process parameters, the hybrid of Extreme Learning Machine and Particle Swarm Optimization is applied. This modelling technique tend to converge faster than other artificial intelligent technique and give accurate result. For the optimization stage, again the Particle Swarm Optimization is used to get the optimal cutting parameters based on the performance function preferred by the manufacturer. Overall, the system can reduce the gap between academic world and the industry by introducing a simple yet easy to implement optimization technique. This novel optimization technique can give accurate result besides being the fastest technique. � Published under licence by IOP Publishing Ltd.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo12086
dc.identifier.doi10.1088/1757-899X/342/1/012086
dc.identifier.issue1
dc.identifier.scopus2-s2.0-85046277566
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85046277566&doi=10.1088%2f1757-899X%2f342%2f1%2f012086&partnerID=40&md5=7a4342052756eac4a78a9d6bfd107416
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23847
dc.identifier.volume342
dc.publisherInstitute of Physics Publishingen_US
dc.relation.ispartofAll Open Access, Bronze
dc.sourceScopus
dc.sourcetitleIOP Conference Series: Materials Science and Engineering
dc.titleExtreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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