Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization
Edgar O. Reséndiz-Flores,
Gerardo Altamirano-Guerrero,
Patricia S. Costa,
Antonio E. Salas-Reyes,
Armando Salinas-Rodríguez,
Frank Goodwin
Affiliations
Edgar O. Reséndiz-Flores
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Saltillo, Blvd. Venustiano Carranza 2400, Col. Tecnológico, Saltillo 25280, Coahuila, Mexico
Gerardo Altamirano-Guerrero
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de Saltillo, Blvd. Venustiano Carranza 2400, Col. Tecnológico, Saltillo 25280, Coahuila, Mexico
Patricia S. Costa
Consultores Asociados en Soldadura S.C., Saltillo 25730, Coahuila, Mexico
Antonio E. Salas-Reyes
Departamento de Ingeniería Metalúrgica, Facultad de Química, UNAM, Circuito de la Investigación Científica S/N, Ciudad Universitaria, Coyoacán, Mexico City 04510, Mexico
Armando Salinas-Rodríguez
Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Unidad Saltillo, Av. Industria Metalúrgica 1062, Parque Industrial Saltillo, Ramos Arizpe 25900, Coahuila, Mexico
Frank Goodwin
International Zinc Association, 2530 Meridian Parkway, Durham, NC 27713, USA
This modeling and optimization study applies a non-linear back-propagation artificial neural network, commonly denoted as BPNN, to model the most important mechanical properties such as yield strength (YS), ultimate tensile strength (UTS) and elongation at fracture (EL) during the experimental processing of hot-dip galvanized dual-phase (GDP) steels. Once the non-linear BPNN is properly trained, the most important variables of the continuous galvanizing process, including initial/first cooling rate (CR1), holding time at the galvanizing temperature of 460 °C (tg) and the final/second cooling rate (CR2), are obtained in an optimal way using an evolutionary approach. The experimental development of GDP steels in continuous processing lines with outstanding mechanical properties (550 YS UTS and 10% EL) is possible by using a combined hybrid approach based in BPNN and multi-objective genetic algorithm (GA). The proposed computational method is applied to the specific design of an actual manufacturing process for the first time.
