Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling
In 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 deri...
- Autores:
-
Xia, J.S.
Khabaz, Mohamad Khaje
Patra, Indrajit
Khalid, Imran
Núñez Álvarez, José Ricardo
Rahmanian, Alireza
Eftekhari, S. Ali
Toghraie, Davood
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9928
- Acceso en línea:
- https://hdl.handle.net/11323/9928
https://repositorio.cuc.edu.co/
- Palabra clave:
- Tandem cold rolling
Perceptron feed-forward ANN
Rolling power and slip prediction
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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| dc.title.eng.fl_str_mv |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| title |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| spellingShingle |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling Tandem cold rolling Perceptron feed-forward ANN Rolling power and slip prediction |
| title_short |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| title_full |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| title_fullStr |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| title_full_unstemmed |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| title_sort |
Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling |
| dc.creator.fl_str_mv |
Xia, J.S. Khabaz, Mohamad Khaje Patra, Indrajit Khalid, Imran Núñez Álvarez, José Ricardo Rahmanian, Alireza Eftekhari, S. Ali Toghraie, Davood |
| dc.contributor.author.none.fl_str_mv |
Xia, J.S. Khabaz, Mohamad Khaje Patra, Indrajit Khalid, Imran Núñez Álvarez, José Ricardo Rahmanian, Alireza Eftekhari, S. Ali Toghraie, Davood |
| dc.subject.proposal.eng.fl_str_mv |
Tandem cold rolling Perceptron feed-forward ANN Rolling power and slip prediction |
| topic |
Tandem cold rolling Perceptron feed-forward ANN Rolling power and slip prediction |
| description |
In 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. |
| publishDate |
2023 |
| dc.date.accessioned.none.fl_str_mv |
2023-02-28T16:45:20Z |
| dc.date.available.none.fl_str_mv |
2023-02-28T16:45:20Z 2025 |
| dc.date.issued.none.fl_str_mv |
2023 |
| dc.type.spa.fl_str_mv |
Artículo de revista |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Text |
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info:eu-repo/semantics/article |
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http://purl.org/redcol/resource_type/ART |
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info:eu-repo/semantics/publishedVersion |
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http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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J.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.identifier.issn.spa.fl_str_mv |
0019-0578 |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/11323/9928 |
| dc.identifier.doi.none.fl_str_mv |
10.1016/j.isatra.2022.06.009 |
| dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
| dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
| dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
| identifier_str_mv |
J.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. 0019-0578 10.1016/j.isatra.2022.06.009 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/9928 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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ISA Transactions |
| dc.relation.references.spa.fl_str_mv |
[1] Yang J-m, Zhang Q, Che H-j, Han X-y. Multi-objective optimization for tandem cold rolling schedule. J Iron Steel Res Int 2010;17(11):34–9. [2] Chen H, et al. Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System. J Web Eng 2021;20(2). [3] Zhang Y, et al. Review on deep learning applications in frequency analysis and control of modern power system. Int J Electr Power Energy Syst 2022;136:107744. [4] Lv Z, et al. Safety Poka Yoke in Zero-Defect Manufacturing Based on Digital Twins. IEEE Trans Industr Inform 2022;1. http://dx.doi.org/10.1109/TII.2021.3139897. [5] Ruhani B, et al. Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data. Physica A 2019;525:616–27. [6] Dixit U, Chandra S. A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process. Int J Adv Manuf Technol 2003;22(11–12):883–9. [7] Rath S, Singh A, Bhaskar U, Krishna B, Santra B, Rai D, et al. Artificial neural network modeling for prediction of roll force during plate rolling process. Mater Manuf Process 2010;25(1–3):149–53. [8] Kim D, Kim Y, Kim B. Optimization of the irregular shape rolling process with an artificial neural network. J Mater Process Technol 2001;113(1–3):131–5. [9] Ghaisari J, Jannesari H, Vatani M. Artificial neural network predictors for mechanical properties of cold rolling products. Adv Eng Softw 2012;45(1):91–9. [10] Peng Y, Liu H, Du R. A neural network-based shape control system for cold rolling operations. J Mater Process Technol 2008;202(1–3):54–60. [11] Mahmoodkhani Y, Wells M, Song G. Prediction of roll force in skin pass rolling using numerical and artificial neural network methods. Ironmak Steelmak 2017;44(4):281–6. [12] Asl YD, Woo YY, Kim Y, Moon YH. Non-sorting multi-objective optimization of flexible roll forming using artificial neural networks. Int J Adv Manuf Technol 2020;107(5):2875–88. [13] Esendağ K, Orta AH, Kayabaşı İ, İlker S. Prediction of reversible cold rolling process parameters with artificial neural network and regression models for industrial applications: A case study. Procedia CIRP 2019;79:644–8. [14] Orta AH, Kayabasi I, Senol M. Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks. Ironmak Steelmak 2019. [15] Cho S, Cho Y, Yoon S. Reliable roll force prediction in cold mill using multiple neural networks. IEEE Trans Neural Netw 1997;8(4):874–82. [16] Kang G-W, Liu H-B, editors. Surface defects inspection of cold rolled strips based on neural network. In: 2005 international conference on machine learning and cybernetics. IEEE; 2005. [17] Mohanty* I, Datta S, Bhattacharjee D. Composition–processing–property correlation of cold-rolled IF steel sheets using neural network. Mater Manuf Process 2008;24(1):100–5. [18] Lu X, Sun J, Song Z, Li G, Wang Z, Hu Y, et al. Prediction and analysis of cold rolling mill vibration based on a data-driven method. Appl Soft Comput 2020;96:106706. [19] Larkiola J, Myllykoski P, Nylander J, Korhonen A. Prediction of rolling force in cold rolling by using physical models and neural computing. J Mater Process Technol 1996;60(1–4):381–6. [20] Gudur P, Dixit U. A neural network-assisted finite element analysis of cold flat rolling. Eng Appl Artif Intell 2008;21(1):43–52. [21] Heidari A, Forouzan MR. Optimization of cold rolling process parameters in order to increasing rolling speed limited by chatter vibrations. J Adv Res 2013;4(1):27–34. [22] Zhao C, Li C, Hu L. Rolling and sliding between non-spherical particles. Physica A 2018;492:181–91. [23] Guzelbey IH, Cevik A, Erklig A. Prediction of web crippling strength of cold-formed steel sheetings using neural networks. J Constr Steel Res 2006;62(10):962–73. [24] Hemmat Esfe M, Hajian M, Toghraie D, Khaje khabaz M, Rahmanian A, Pirmoradian M, et al. Prediction the dynamic viscosity of MWCNTAl2o3 (30:70)/ oil 5W50 hybrid nano-lubricant using principal component analysis (PCA) with artificial neural network (ANN). Egypt Inform J 2022. [25] Wang Q, Sun J, Li X, Wang Z, Wang P, Zhang D. Analysis of lateral metal flow-induced flatness deviations of rolled steel strip: mathematical modeling and simulation experiments. Appl Math Model 2020;77:289–308. [26] Zárate LE, Dias SM. Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method. Eng Appl Artif Intell 2009;22(4–5):718–31. [27] Babajamali Z, Khaje khabaz M, Aghadavoudi F, Farhatnia F, Eftekhari SA, Toghraie D. Pareto multi-objective optimization of tandem cold rolling settings for reductions and inter stand tensions using NSGA-II. ISA Trans 2022. [28] Khaje khabaz M, Eftekhari A, Hashemian M. Free vibration analysis of sandwich micro beam with piezoelectric based on modified couple stress theory and surface effects. J Simul Anal Nov Technol Mech Eng 2018;10(4):33–48. [29] Pittner J, Simaan MA. Tandem cold metal rolling mill control: using practical advanced methods. Springer Science & Business Media; 2010. [30] Zhao H. Regenerative chatter in cold rolling. Northwestern University; 2008. [31] Tieu A, Liu Y. Friction variation in the cold-rolling process. Tribol Int 2004;37(2):177–83. [32] Dixit U, Dixit P. Application of fuzzy set theory in the scheduling of a tandem cold-rolling mill. J Manuf Sci Eng 2000;122(3):494–500. [33] Whitton P, Ford H. Surface friction and lubrication in cold strip rolling. Proc Inst Mech Eng 1955;169(1):123–40. [34] Poursina M, Dehkordi NT, Fattahi A, Mirmohammadi H. Application of genetic algorithms to optimization of rolling schedules based on damage mechanics. Simul Model Pract Theory 2012;22:61–73. [35] Reddy NV, Suryanarayana G. A set-up model for tandem cold rolling mills. J Mater Process Technol 2001;116(2–3):269–77. [36] Farhatnia F, Salimi M. Effect of entry bending moment on exit curvature in asymmetrical rolling. Int J Eng Sci Technol 2011;3(4). [37] Gunasekera J, Jia Z, Malas J, Rabelo L. Development of a neural network model for a cold rolling process. Eng Appl Artif Intell 1998;11(5):597–603. [38] Tato A, Nkambou R. Improving adam optimizer. 2018. [39] Nikbakht S, Anitescu C, Rabczuk T. Optimizing the neural network hyperparameters utilizing genetic algorithm. J Zhejiang Univ Sci A 2021;22(6):407–26. [40] He S, Wu Q, Saunders J, editors. A group search optimizer for neural network training. In: International conference on computational science and its applications. Springer; 2006. [41] Li H, Deng J, Yuan S, Feng P, Arachchige DD. Monitoring and identifying wind turbine generator bearing faults using deep belief network and EWMA control charts. Front Energy Res 2021;770. [42] Li H, Deng J, Feng P, Pu C, Arachchige DD, Cheng Q. Short-term nacelle orientation forecasting using bilinear transformation and ICEEMDAN framework. Front Energy Res 2021;697. [43] Khaje khabaz M, Eftekhari SA, Toghraie D. Vibration and dynamic analysis of a cantilever sandwich microbeam integrated with piezoelectric layers based on strain gradient theory and surface effects. Appl Math Comput 2022;419(C). [44] Rahmanian A, Mireei SA, Sadri S, Gholami M, Nazeri M. Application of biospeckle laser imaging for early detection of chilling and freezing disorders in orange. Postharvest Biol Technol 2020;162:111118. |
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© 2022 ISA. Published by Elsevier Ltd. All rights reserved. |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0) |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2022 ISA. Published by Elsevier Ltd. All rights reserved.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfXia, J.S.Khabaz, Mohamad KhajePatra, IndrajitKhalid, ImranNúñez Álvarez, José RicardoRahmanian, AlirezaEftekhari, S. AliToghraie, Davood2023-02-28T16:45:20Z20252023-02-28T16:45:20Z2023J.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.2022.06.009.0019-0578https://hdl.handle.net/11323/992810.1016/j.isatra.2022.06.009Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In 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.11 páginasapplication/pdfengISA - Instrumentation, Systems, and Automation SocietyUnited Stateshttps://www.sciencedirect.com/science/article/pii/S0019057822003147Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rollingArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85ISA Transactions[1] Yang J-m, Zhang Q, Che H-j, Han X-y. Multi-objective optimization for tandem cold rolling schedule. J Iron Steel Res Int 2010;17(11):34–9.[2] Chen H, et al. Intelligent Model-based Integrity Assessment of Nonstationary Mechanical System. J Web Eng 2021;20(2).[3] Zhang Y, et al. Review on deep learning applications in frequency analysis and control of modern power system. Int J Electr Power Energy Syst 2022;136:107744.[4] Lv Z, et al. Safety Poka Yoke in Zero-Defect Manufacturing Based on Digital Twins. IEEE Trans Industr Inform 2022;1. http://dx.doi.org/10.1109/TII.2021.3139897.[5] Ruhani B, et al. Statistical investigation for developing a new model for rheological behavior of Silica–ethylene glycol/Water hybrid Newtonian nanofluid using experimental data. Physica A 2019;525:616–27.[6] Dixit U, Chandra S. A neural network based methodology for the prediction of roll force and roll torque in fuzzy form for cold flat rolling process. Int J Adv Manuf Technol 2003;22(11–12):883–9.[7] Rath S, Singh A, Bhaskar U, Krishna B, Santra B, Rai D, et al. Artificial neural network modeling for prediction of roll force during plate rolling process. Mater Manuf Process 2010;25(1–3):149–53.[8] Kim D, Kim Y, Kim B. Optimization of the irregular shape rolling process with an artificial neural network. J Mater Process Technol 2001;113(1–3):131–5.[9] Ghaisari J, Jannesari H, Vatani M. Artificial neural network predictors for mechanical properties of cold rolling products. Adv Eng Softw 2012;45(1):91–9.[10] Peng Y, Liu H, Du R. A neural network-based shape control system for cold rolling operations. J Mater Process Technol 2008;202(1–3):54–60.[11] Mahmoodkhani Y, Wells M, Song G. Prediction of roll force in skin pass rolling using numerical and artificial neural network methods. Ironmak Steelmak 2017;44(4):281–6.[12] Asl YD, Woo YY, Kim Y, Moon YH. Non-sorting multi-objective optimization of flexible roll forming using artificial neural networks. Int J Adv Manuf Technol 2020;107(5):2875–88.[13] Esendağ K, Orta AH, Kayabaşı İ, İlker S. Prediction of reversible cold rolling process parameters with artificial neural network and regression models for industrial applications: A case study. Procedia CIRP 2019;79:644–8.[14] Orta AH, Kayabasi I, Senol M. Prediction of mechanical properties of cold rolled and continuous annealed steel grades via analytical model integrated neural networks. Ironmak Steelmak 2019.[15] Cho S, Cho Y, Yoon S. Reliable roll force prediction in cold mill using multiple neural networks. IEEE Trans Neural Netw 1997;8(4):874–82.[16] Kang G-W, Liu H-B, editors. Surface defects inspection of cold rolled strips based on neural network. In: 2005 international conference on machine learning and cybernetics. IEEE; 2005.[17] Mohanty* I, Datta S, Bhattacharjee D. Composition–processing–property correlation of cold-rolled IF steel sheets using neural network. Mater Manuf Process 2008;24(1):100–5.[18] Lu X, Sun J, Song Z, Li G, Wang Z, Hu Y, et al. Prediction and analysis of cold rolling mill vibration based on a data-driven method. Appl Soft Comput 2020;96:106706.[19] Larkiola J, Myllykoski P, Nylander J, Korhonen A. Prediction of rolling force in cold rolling by using physical models and neural computing. J Mater Process Technol 1996;60(1–4):381–6.[20] Gudur P, Dixit U. A neural network-assisted finite element analysis of cold flat rolling. Eng Appl Artif Intell 2008;21(1):43–52.[21] Heidari A, Forouzan MR. Optimization of cold rolling process parameters in order to increasing rolling speed limited by chatter vibrations. J Adv Res 2013;4(1):27–34.[22] Zhao C, Li C, Hu L. Rolling and sliding between non-spherical particles. Physica A 2018;492:181–91.[23] Guzelbey IH, Cevik A, Erklig A. Prediction of web crippling strength of cold-formed steel sheetings using neural networks. J Constr Steel Res 2006;62(10):962–73.[24] Hemmat Esfe M, Hajian M, Toghraie D, Khaje khabaz M, Rahmanian A, Pirmoradian M, et al. Prediction the dynamic viscosity of MWCNTAl2o3 (30:70)/ oil 5W50 hybrid nano-lubricant using principal component analysis (PCA) with artificial neural network (ANN). Egypt Inform J 2022.[25] Wang Q, Sun J, Li X, Wang Z, Wang P, Zhang D. Analysis of lateral metal flow-induced flatness deviations of rolled steel strip: mathematical modeling and simulation experiments. Appl Math Model 2020;77:289–308.[26] Zárate LE, Dias SM. Qualitative behavior rules for the cold rolling process extracted from trained ANN via the FCANN method. Eng Appl Artif Intell 2009;22(4–5):718–31.[27] Babajamali Z, Khaje khabaz M, Aghadavoudi F, Farhatnia F, Eftekhari SA, Toghraie D. Pareto multi-objective optimization of tandem cold rolling settings for reductions and inter stand tensions using NSGA-II. ISA Trans 2022.[28] Khaje khabaz M, Eftekhari A, Hashemian M. Free vibration analysis of sandwich micro beam with piezoelectric based on modified couple stress theory and surface effects. J Simul Anal Nov Technol Mech Eng 2018;10(4):33–48.[29] Pittner J, Simaan MA. Tandem cold metal rolling mill control: using practical advanced methods. Springer Science & Business Media; 2010.[30] Zhao H. Regenerative chatter in cold rolling. Northwestern University; 2008.[31] Tieu A, Liu Y. Friction variation in the cold-rolling process. Tribol Int 2004;37(2):177–83.[32] Dixit U, Dixit P. Application of fuzzy set theory in the scheduling of a tandem cold-rolling mill. J Manuf Sci Eng 2000;122(3):494–500.[33] Whitton P, Ford H. Surface friction and lubrication in cold strip rolling. Proc Inst Mech Eng 1955;169(1):123–40.[34] Poursina M, Dehkordi NT, Fattahi A, Mirmohammadi H. Application of genetic algorithms to optimization of rolling schedules based on damage mechanics. Simul Model Pract Theory 2012;22:61–73.[35] Reddy NV, Suryanarayana G. A set-up model for tandem cold rolling mills. J Mater Process Technol 2001;116(2–3):269–77.[36] Farhatnia F, Salimi M. Effect of entry bending moment on exit curvature in asymmetrical rolling. Int J Eng Sci Technol 2011;3(4).[37] Gunasekera J, Jia Z, Malas J, Rabelo L. Development of a neural network model for a cold rolling process. Eng Appl Artif Intell 1998;11(5):597–603.[38] Tato A, Nkambou R. Improving adam optimizer. 2018.[39] Nikbakht S, Anitescu C, Rabczuk T. Optimizing the neural network hyperparameters utilizing genetic algorithm. J Zhejiang Univ Sci A 2021;22(6):407–26.[40] He S, Wu Q, Saunders J, editors. A group search optimizer for neural network training. In: International conference on computational science and its applications. Springer; 2006.[41] Li H, Deng J, Yuan S, Feng P, Arachchige DD. Monitoring and identifying wind turbine generator bearing faults using deep belief network and EWMA control charts. Front Energy Res 2021;770.[42] Li H, Deng J, Feng P, Pu C, Arachchige DD, Cheng Q. Short-term nacelle orientation forecasting using bilinear transformation and ICEEMDAN framework. Front Energy Res 2021;697.[43] Khaje khabaz M, Eftekhari SA, Toghraie D. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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