Data-Driven Pareto Optimization for Microalloyed Steels Using Genetic Algorithms

A data base was put together for the mechanical properties of microalloyed steels, which contained about 800 entries for ultimate tensile strength (UTS), yield strength (YS), and elongation. Using an evolutionary neural network, based upon a predatorprey genetic algorithms of bi-objective type, this information was used to construct data-driven models for UTS, YS, and elongation. The optimum Pareto tradeoffs between these properties were obtained using a multi-objective genetic algorithm. The results led to some hitherto unexplored steel compositions with optimum properties. Some such steels were actually cast and the experimentally observed property values were found to be well in accord with the predicted results.