Optimal Design of Hot-Dip Galvanized DP Steels via Artificial Neural Networks and Multi-Objective Genetic Optimization

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 yield strength (YS), ultimate tensile (UTS) elongation at fracture (EL) during experimental processing of hot-dip galvanized dual-phase (GDP) steels. Once BPNN is properly trained, variables continuous galvanizing process, including initial/first cooling rate (CR1), holding time temperature 460 °C (tg) final/second (CR2), are obtained in an optimal way using evolutionary approach. The development GDP steels lines with outstanding (550 < YS 750 MPa, 1100 MPa UTS 10% EL) possible by combined hybrid approach based multi-objective genetic algorithm (GA). proposed computational method applied specific design actual manufacturing process for first time.