An artificial intelligence-based optimization framework for the optimal composition and thermomechanical processing schedule for specialized micro-alloyed multiphase steels

Abstract

An artificial intelligence-based heuristic approach is presented to optimize the chemical composition and the thermomechanical processing schedule to obtain specialized micro-alloyed multiphase steels with desired mechanical properties, at minimal manufacturing cost. The optimization framework uses a modified form of genetic algorithm, called the micro-genetic algorithm (μGA), that uses a penalty-based cost function formulation operating on a multi-dimensional search space spanning 15 alloying elements, an average cooling temperature, an austenitizing temperature and eight time–temperature points from the cooling profiles of multiphase steels. With superior search speed and convergence rates to the traditional genetic algorithm, μGA uses a neural network-based reduced-order model to predict hardness. Additional correlation equations are used to determine the corresponding tensile strength and elongation. Microstructural analysis was performed using neurocomputing techniques to further validate the accuracy of the algorithm. The entire computational framework was validated using data from the literature, establishing its utility in steel design.