• Altamirano-Guerrero, Gerardo
• Salinas-Rodríguez, Armando
• Goodwin, Frank
• Salas-Reyes, Antonio E.
• Costa, Patricia S.
• Reséndiz-Flores, Edgar O.

Abstract

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 < 750 MPa, 1100 MPa < 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.

Topics

• impedance spectroscopy
• phase
• strength
• steel
• thermogravimetry
• yield strength
• tensile strength