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- Title
Optimal design of thermal cycles for experimental processing of advanced TRIP-assisted galvanized steels using support vector regression and kernel-based gradient evolution method.
- Authors
Flor-Sánchez, Carlos O.; Reséndiz-Flores, Edgar O.; Altamirano-Guerrero, Gerardo
- Abstract
The thermal cycles necessary to produce galvanized transformation-induced plasticity (TRIP) steels on a continuous hot-dip galvanizing line involve several processing parameters that must be chosen and strictly controlled to achieve the required mechanical properties. The resulting mechanical properties strongly depend on the optimal determination of each heat treatment variable. In this paper, a novel memetic algorithm named kernel-based gradient evolution (KGE) 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, the most significant processing parameters (cooling rate after intercritical austenitizing, isothermal holding time at the galvanizing temperature in the bainitic region, and last cooling rate to room temperature) were thus optimized to achieve the required hardness values. In general, SVR fits in a satisfactory manner the relation between experimental parameters and resulting hardness which is highly non-linear in nature, and hence, it is used as objective function. Besides, KGE algorithm reveals an impressive performance since it solved the problem in less than 10 iterations. As a result, optimal solution is successfully achieved by the proposed SVR-KGE model.
- Subjects
GALVANIZED steel; THERMOCYCLING; HEAT treatment; GALVANIZING; HARDNESS
- Publication
International Journal of Advanced Manufacturing Technology, 2023, Vol 128, Issue 3/4, p1379
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-023-11926-9