Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation
Computer 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 funct...
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2023
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my.uniten.dspace-238472023-05-29T14:52:21Z Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation Janahiraman T.V. Ahmad N. Nordin F.H. 35198314400 56486827000 25930510500 Computer 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 intelligence The 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. Final 2023-05-29T06:52:21Z 2023-05-29T06:52:21Z 2018 Conference Paper 10.1088/1757-899X/342/1/012086 2-s2.0-85046277566 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046277566&doi=10.1088%2f1757-899X%2f342%2f1%2f012086&partnerID=40&md5=7a4342052756eac4a78a9d6bfd107416 https://irepository.uniten.edu.my/handle/123456789/23847 342 1 12086 All Open Access, Bronze Institute of Physics Publishing Scopus |
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Computer 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 intelligence |
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35198314400 |
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35198314400 Janahiraman T.V. Ahmad N. Nordin F.H. |
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Conference Paper |
author |
Janahiraman T.V. Ahmad N. Nordin F.H. |
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Janahiraman T.V. Ahmad N. Nordin F.H. Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
author_sort |
Janahiraman T.V. |
title |
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
title_short |
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
title_full |
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
title_fullStr |
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
title_full_unstemmed |
Extreme Learning Machine and Particle Swarm Optimization in optimizing CNC turning operation |
title_sort |
extreme learning machine and particle swarm optimization in optimizing cnc turning operation |
publisher |
Institute of Physics Publishing |
publishDate |
2023 |
_version_ |
1806427345043587072 |
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13.214268 |