Predicting surface roughness in turning operation using extreme learning machine
Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the al...
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| Format: | Conference Paper |
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Trans Tech Publications Ltd
2023
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| _version_ | 1833351089619992576 |
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| author | Nooraziah A. Tiagrajah V.J. |
| author2 | 55263605500 |
| author_facet | 55263605500 Nooraziah A. Tiagrajah V.J. |
| author_sort | Nooraziah A. |
| building | UNITEN Library |
| collection | Institutional Repository |
| content_provider | Universiti Tenaga Nasional |
| content_source | UNITEN Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection. © (2014) Trans Tech Publications, Switzerland. |
| format | Conference Paper |
| id | my.uniten.dspace-22102 |
| institution | Universiti Tenaga Nasional |
| publishDate | 2023 |
| publisher | Trans Tech Publications Ltd |
| record_format | dspace |
| spelling | my.uniten.dspace-221022023-05-16T10:47:24Z Predicting surface roughness in turning operation using extreme learning machine Nooraziah A. Tiagrajah V.J. 55263605500 35198314400 Prediction model allows the machinist to determine the values of the cutting performance before machining. According to literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Recently, Extreme Learning Machine (ELM) has been introduced as the alternative to overcome the limitation from the previous methods. ELM has similar structure as single hidden layer feedforward neural network with analytically to determine output weight. By comparing to Response Surface Methodology, Support Vector Machine and Neural Network, this paper proposed the prediction of surface roughness using ELM method. The result indicates that ELM can yield satisfactory solution for predicting surface roughness in term of training speed and parameter selection. © (2014) Trans Tech Publications, Switzerland. Final 2023-05-16T02:47:24Z 2023-05-16T02:47:24Z 2014 Conference Paper 10.4028/www.scientific.net/AMM.554.431 2-s2.0-84903549200 https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903549200&doi=10.4028%2fwww.scientific.net%2fAMM.554.431&partnerID=40&md5=5bdd52f3d9a21ab5c289f4cf4ace7904 https://irepository.uniten.edu.my/handle/123456789/22102 554 431 435 Trans Tech Publications Ltd Scopus |
| spellingShingle | Nooraziah A. Tiagrajah V.J. Predicting surface roughness in turning operation using extreme learning machine |
| title | Predicting surface roughness in turning operation using extreme learning machine |
| title_full | Predicting surface roughness in turning operation using extreme learning machine |
| title_fullStr | Predicting surface roughness in turning operation using extreme learning machine |
| title_full_unstemmed | Predicting surface roughness in turning operation using extreme learning machine |
| title_short | Predicting surface roughness in turning operation using extreme learning machine |
| title_sort | predicting surface roughness in turning operation using extreme learning machine |
| url_provider | http://dspace.uniten.edu.my/ |