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International Journal of Computer Integrated Manufacturing Volume 26, 2013 - Issue 8
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Articles

Modelling hot rolling manufacturing process using soft computing techniques

Hossam Faris Department of Business Information Systems, The University of Jordan, Amman, JordanCorrespondence[email protected]
,
Alaa Sheta College of Computers and Information Systems, Taif University, Taif, Saudi Arabia
&
Ertan Öznergiz Marine Engineering Operations Department, Faculty of Naval Architecture and Maritime, Yildiz Technical University, Istanbul, Turkey
Pages 762-771 | Received 01 May 2012, Accepted 03 Jan 2013, Published online: 08 Mar 2013

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Estela Ruiz, Diego Ferreño, Miguel Cuartas, Ana López, Valentín Arroyo & Federico Gutiérrez-Solana. (2020) Machine learning algorithms for the prediction of the strength of steel rods: an example of data-driven manufacturing in steelmaking. International Journal of Computer Integrated Manufacturing 33:9, pages 880-894.
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Hossam Faris & Alaa Sheta. (2016) A comparison between parametric and non-parametric soft computing approaches to model the temperature of a metal cutting tool. International Journal of Computer Integrated Manufacturing 29:1, pages 64-75.
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Hossam Faris, Alaa F. Sheta & Ertan Öznergiz. (2016) MGP–CC: a hybrid multigene GP–Cuckoo search method for hot rolling manufacture process modelling. Systems Science & Control Engineering 4:1, pages 39-49.
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Articles from other publishers (9)

Malik Braik, Alaa Sheta, Heba Al-Hiary & Sultan Aljahdali. (2022) Enhanced cuckoo search algorithm for industrial winding process modeling. Journal of Intelligent Manufacturing 34:4, pages 1911-1940.
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Marios Kefalas, Juan de Santiago RojoJr.Jr., Asteris Apostolidis, Dirk van den Herik, Bas van Stein & Thomas Bäck. (2022) Explainable Artificial Intelligence for Exhaust Gas Temperature of Turbofan Engines. Journal of Aerospace Information Systems 19:6, pages 447-454.
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Ziyu Hu, Zhihui Wei, Hao Sun, Jingming Yang & Lixin Wei. (2019) Optimization of Metal Rolling Control Using Soft Computing Approaches: A Review. Archives of Computational Methods in Engineering 28:2, pages 405-421.
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Riku-Pekka Nikula & Kauko Leiviskä. (2020) Roller Leveler Monitoring Using Acceleration Measurements and Models for Steel Strip Properties. Machines 8:3, pages 43.
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Miha Kovačič & Uroš Župerl. (2020) Genetic programming in the steelmaking industry. Genetic Programming and Evolvable Machines 21:1-2, pages 99-128.
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Rana Faris, Bara’a Almasri, Hossam Faris, Faris M. AL-Oqla & Doraid Dalalah. 2020. Evolutionary Machine Learning Techniques. Evolutionary Machine Learning Techniques 113 127 .
Riku-Pekka Nikula, Konsta Karioja, Kauko Leiviskä & Esko Juuso. (2017) Prediction of mechanical stress in roller leveler based on vibration measurements and steel strip properties. Journal of Intelligent Manufacturing 30:4, pages 1563-1579.
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Ali Rodan, Alaa F. Sheta & Hossam Faris. (2016) Bidirectional reservoir networks trained using SVM $$+$$ + privileged information for manufacturing process modeling. Soft Computing 21:22, pages 6811-6824.
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Walid M. Aly & Alaa Sheta. 2013. Research and Development in Intelligent Systems XXX. Research and Development in Intelligent Systems XXX 443 449 .

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