Publication:
Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation

Simple item page
dc.contributor.authorJanahiraman T.V.en_US
dc.contributor.authorAhmad N.en_US
dc.contributor.authorid35198314400en_US
dc.contributor.authorid56486827000en_US
dc.date.accessioned2023-05-29T06:00:45Z
dc.date.available2023-05-29T06:00:45Z
dc.date.issued2015
dc.descriptionCarbon; Carbon steel; Computer control systems; Electric power utilization; Knowledge acquisition; Learning systems; Machining centers; Particle swarm optimization (PSO); Statistical tests; Steel testing; Turning; Computer numerical control; Extreme learning machine; Machining efficiency; Machining parameters; Mean absolute percentage error; Optimal machining parameters; Performance analysis; Training and testing; Surface roughnessen_US
dc.description.abstractThe turning operation in the Computer Numerical Control (CNC) needs optimal machining parameters to achieve higher machining efficiency. The selection of machining parameters is very important to find the best performances in machining process. In this study, two different architectures of particle swarm optimization based extreme learning machine were analyzed for modelling inputs parameters: feed rate, cutting speed and depth of cut to output parameters: surface roughness and power consumption. The data were collected from 15 experiments using carbon steel AISI 1045 which were separated into training and testing dataset. Our experimental results shows that Architecture II is the most outstanding model with mean absolute percentage error (MAPE) of 0.0469 for predicting the training data and 0.204 for predicting the testing data. � 2014 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7066649
dc.identifier.doi10.1109/ICIMU.2014.7066649
dc.identifier.epage307
dc.identifier.scopus2-s2.0-84937393704
dc.identifier.spage303
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84937393704&doi=10.1109%2fICIMU.2014.7066649&partnerID=40&md5=32817b23a5d6fdef4112f414d04bf5c2
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22402
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.titlePerformance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operationen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS