Exploration of outliers in strength-ductility relationship of dual-phase steels

To overcome the trade-off relationship between tensile strength and elongation of dual-phase steels, three exploratory techniques were utilized: Bayesian optimization (BO), BoundLess Objective-free eXploration (BLOX), and one-class support vector machine (OCSVM). The BO is an optimization method using Gaussian process regression, and the BLOX and OCSVM are designed for finding out-of-trend materials. Initially, a large number of synthetic phase distributions of ferrite and martensite were prepared by the Gaussian random field method. Feature importance analysis was then performed to extract microstructural descriptors used as explanatory variables in the explorations. The results revealed that the volume fraction of martensite, the higher-order principal component of the spatial correlation function, and the first principal component of the persistent homology are important in predicting the product of strength and elongation (TS x EL). In the three exploration methods, crystal plasticity finite element analyses with a ductile damage model were repeatedly performed to predict strength and elongation. The BO and OCSVM successfully found microstructures with TS x EL much higher than those of random search in a limited number of iterations. The microstructures discovered in the explorations indicated that two approaches to material design are effective in improving tensile properties of dual-phase steels: uniformly dispersing the fine hard phase and constructing a lamellar structure of the hard and soft phases. [GRAPHICS]