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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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