Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling

dc.creatorXia, J.S.
dc.creatorKhabaz, Mohamad Khaje
dc.creatorPatra, Indrajit
dc.creatorKhalid, Imran
dc.creatorNúñez Álvarez, José Ricardo
dc.creatorRahmanian, Alireza
dc.creatorEftekhari, S. Ali
dc.creatorToghraie, Davood
dc.date2023-02-28T16:45:20Z
dc.date2025
dc.date2023-02-28T16:45:20Z
dc.date2023
dc.date.accessioned2023-10-03T19:27:00Z
dc.date.available2023-10-03T19:27:00Z
dc.identifierJ.S. Xia, Mohamad Khaje Khabaz, Indrajit Patra, Imran Khalid, José Ricardo Nuñez Alvarez, Alireza Rahmanian, S. Ali Eftekhari, Davood Toghraie, Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling, ISA Transactions, Volume 132, 2023, Pages 353-363, ISSN 0019-0578, https://doi.org/10.1016/j.isatra.2022.06.009.
dc.identifier0019-0578
dc.identifierhttps://hdl.handle.net/11323/9928
dc.identifier10.1016/j.isatra.2022.06.009
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9170137
dc.descriptionIn this paper, an Artificial Neural Network (ANN) is used to investigate the influence of rolling parameters such as thickness reduction, inter-strand tension, rolling speed and friction on the rolling force, rolling power, and slip of tandem cold rolling. For this reason, the rolling power was derived for 195 various experiments through a series of observation tests. The network is trained and tested using real data collected from a practical tandem rolling line. The best topology of the ANN is determined by Broyden–Fletcher–Goldfarb–Shanno (BFGS) training algorithm and error, and nine neurons in the hidden layer had the best performance. The average of the training, testing, and validating correlation coefficients data sets are mentioned 0.947, 0.924, and 0.943, respectively. The obtained results show MSE value 4.2 × 10−4 for predicting slip. In addition, the effect of friction and angular velocity condition on the cold rolling critical slip phenomena are investigated. The results show that ANNs can accurately predict the cold rolling parameters considered in this study.
dc.format11 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherISA - Instrumentation, Systems, and Automation Society
dc.publisherUnited States
dc.relationISA Transactions
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dc.rights© 2022 ISA. Published by Elsevier Ltd. All rights reserved.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rightshttp://purl.org/coar/access_right/c_f1cf
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S0019057822003147
dc.subjectTandem cold rolling
dc.subjectPerceptron feed-forward ANN
dc.subjectRolling power and slip prediction
dc.titleUsing feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_2df8fbb1
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
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