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Multi-Objective Optimization of the Hot Rolling Scheduling of Steel Using a Genetic Algorithm

Published online by Cambridge University Press:  19 November 2019

Carlos A Hernández Carreón*
Affiliation:
Instituto Politécnico Nacional. ESIME Azcapotzalco, Sección de Estudios de Posgrado e Investigación. Av. de las Granjas, 682. Col. Santa Catarina, Azcapotzalco, Ciudad de México
Juana E Mancilla Tolama
Affiliation:
Instituto Politécnico Nacional. ESIME Azcapotzalco, Sección de Estudios de Posgrado e Investigación. Av. de las Granjas, 682. Col. Santa Catarina, Azcapotzalco, Ciudad de México
Guadalupe Castilla Valdez
Affiliation:
Instituto Tecnológico de Ciudad Madero. 1o. de Mayo y Sor Juana I. de la Cruz S/N. 89440. Cd. Madero, Tamaulipas, México
Iván Hernández González
Affiliation:
Instituto Politécnico Nacional. ESIME Azcapotzalco, Sección de Estudios de Posgrado e Investigación. Av. de las Granjas, 682. Col. Santa Catarina, Azcapotzalco, Ciudad de México
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Abstract

The hot rolling process reduces a slab passing through a series of work-rolls to obtain a strip of target thickness. Developing robust, efficient, and accurate simulation methods improve the process. This research aims to minimize the hot rolling time, bending of work rolls, thermal crown, and wear of work rolls, subject to some process constraints. The problem solution is by using a multi-objective genetic algorithm with four function objectives. The second generation of the Non-dominated Sorting Genetic Algorithm was chosen to solve the problem of this research. A probed constitutive model has been incorporated into the algorithm to compute the flow stress as a function of the chemical composition of steels. The algorithm implemented to minimize the four objectives proposed obtained the optimal schedule and associated makespan.

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Articles
Copyright
Copyright © Materials Research Society 2019 

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References

Nolle, L., Armstrong, A., Hopgood, A., and Ware, A., in Computational Intelligence: Theory and Applications International Conference, edited by Reusch, B. (Springer Berlin Heidelberg, Berlin, Heidelberg, 1999), pp. 435-452.CrossRefGoogle Scholar
Chakraborti, N. and Kumar, A., Mater Manuf Proc, 18, 433-445 (2003).CrossRefGoogle Scholar
Hernández Carreón, C. A., Fraire Huacuja, H. J., Espriella Fernandez, K., Valdez Castilla, G., and Mancilla Tolama, J. E. in Innovations in Hybrid Intelligent Systems, edited by Corchado, E., Corchado, J. M., and Abraham, A., (Springer, Berlin Heidelberg, 2007) 44 pp. 247.CrossRefGoogle Scholar
Hernández, Carlos A., Castilla, Guadalupe, López, Alejandro, and Mancilla, Juana E., Res. Comp. Sci. 120, 65-80 (2016).Google Scholar
Chen, S., Li, W., and Liu, X., J Iron Steel Res Int . 22, 777-784 (2015).CrossRefGoogle Scholar
Tseng, A. A., Tong, S. X., and Raudensky, M., and Chen, T. C., Steel Res . 67, 188 (1996).CrossRefGoogle Scholar
Tahir, M., “Some aspects on lubrication and roll wear in rolling mills.” Ph.D. Thesis,” Stockholm: Royal Institute of Technology, KTH (2003).Google Scholar
Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., IEEE Trans. Evol. Comp. 6, 182-197 (2002).CrossRefGoogle Scholar
HSMM rel 3.0, INTEG process group, AISI/DOE Technology, 2006.Google Scholar
Hernández, C. A., Medina, S. F., and Ruiz, J., Acta Metall. Mater. 44 155-163 (1996).CrossRefGoogle Scholar