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 (C R-1), isothermal holding time at the galvanizing temperature in the bainitic region t(2), and last cooling rate to room temperature (C R-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 degrees C/s, 33-37 s, and 1-2 degrees C/s for C R-1, t(2), and C R-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.