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

Carbon; 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...

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Main Authors: Janahiraman T.V., Ahmad N.
Other Authors: 35198314400
Format: Conference Paper
Published: Institute of Electrical and Electronics Engineers Inc. 2023
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spelling my.uniten.dspace-224022023-05-29T14:00:45Z Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation Janahiraman T.V. Ahmad N. 35198314400 56486827000 Carbon; 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 roughness The 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. Final 2023-05-29T06:00:45Z 2023-05-29T06:00:45Z 2015 Conference Paper 10.1109/ICIMU.2014.7066649 2-s2.0-84937393704 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84937393704&doi=10.1109%2fICIMU.2014.7066649&partnerID=40&md5=32817b23a5d6fdef4112f414d04bf5c2 https://irepository.uniten.edu.my/handle/123456789/22402 7066649 303 307 Institute of Electrical and Electronics Engineers Inc. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Carbon; 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 roughness
author2 35198314400
author_facet 35198314400
Janahiraman T.V.
Ahmad N.
format Conference Paper
author Janahiraman T.V.
Ahmad N.
spellingShingle Janahiraman T.V.
Ahmad N.
Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
author_sort Janahiraman T.V.
title Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
title_short Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
title_full Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
title_fullStr Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
title_full_unstemmed Performance analysis of ELM-PSO architectures for modelling surface roughness and power consumption in CNC turning operation
title_sort performance analysis of elm-pso architectures for modelling surface roughness and power consumption in cnc turning operation
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2023
_version_ 1806426685959045120
score 13.214268

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