Multi-objective gradient-based intelligent optimization of ultra-high-strength galvanized TRIP steels

In this paper, a novel gradient-based algorithm named Kernel-based hybrid multi-objective optimization (KHMO) is implemented and coupled with a support vector regression (SVR) model to efficiently optimize the production of a cold rolled hot-dip galvanized TRIP steel. For this purpose, several heat treatments using an isothermal bainitic transformation (IBT) temperature compatible with continuous hot-dip galvanizing were performed. The most significant processing parameters (cooling rate after intercritical austenitizing ( CR_1 ), isothermal holding time at the galvanizing temperature in the bainitic region t_2 , and last cooling rate to room temperature ( CR_2 )) were thus optimized to achieve the required mechanical properties values. In general, SVR model fits in a satisfactory manner the highly non-linear relationship between experimental parameters and resulting mechanical properties; hence, it is used as objective function. Besides, KHMO algorithm reveals an outstanding performance since it found a dense and spread Pareto front. Moreover, the processing window to manufacture TRIP-aided martensitic steels is suggested in a range of 57–63 ^∘ C/s, 33–37 s, and 1–2 ^∘ C/s for CR_1 , t_2 , and CR_2 , respectively. The developed computational methodology for modeling and optimization of operating parameters is successfully applied for the first time in the experimental processing of advanced TRIP steels.