Improvement of Strength–Ductility Balance in Tempered Martensite Steel Using Multi-Objective Bayesian Optimization

Abstract

We attempted to improve the strength–ductility balance in tempered martensite steel using multi-objective Bayesian optimization. The martensite steels were tempered at two stages, and fine and coarse cementite particles were mixed. Water-quenching rather than furnace-cooling between the first and second temper stages provided a better balance of strength and ductility. Moreover, the strength–ductility balance was also improved by tempering at a low temperature in the first stage and a high temperature in the second stage, rather than tempering at a high temperature in the first stage and a low temperature in the second stage. Based on these experimental results, multi-objective Bayesian optimization was used to further improve both tensile strength and total elongation. The strength–ductility balance that is better than the experimental results was achieved with a minimal number of optimization times. Additionally, machine learning suggested that it is crucial to control the average aspect ratio of cementite particles to less than 1.8 and the size of coarse cementite particles to less than 2.0 μm in order to improve the strength–ductility balance.