A Computational-based Approach for the Design of Trip Steels

Abstract

The purpose of this work is to optimize the chemical composition as well as the heat treatment for improving the mechanical performance of the TRIP steel by employing the theoretical models. TRIP steel consists of the microstructure with ferrite, bainite, retained austenite and minor martensite. Austenite contributes directly to the TRIP effect as its transformation to martensite under the external stress. In order to stabilize austenite against the martensitic transformation through the heat treatment, the two-step heat treatment is broadly applied to enrich the carbon and stabilize the austenite. During the first step of the heat treatment, intercritical annealing (IA), a dual phase structure (ferrite+austenite) is achieved. The austenite can be initially stabilized because of the low carbon solubility of ferrite. The bainite isothermal treatment (BIT) leads to the further carbon enrichment of IA-austenite by the formation of carbon-free ferrite. Comparing to the experiments, the thermodynamic and kinetic models are the lower and upper bounds of the carbon content of retained austenite. The mechanical properties are predicted using the swift model based on the predicted microstructure. In this work, a theoretical approach is coupled to a Genetic Algorithm-based optimization procedure to design (1) the heat treated temperatures to maximize the volume fraction of retained austenite in a Fe-0.32C-1.42Mn-1.56Si alloy and the chemical composition of (2) Fe-C-Mn-Si and (3) Fe-C-Mn-Si-Al-Cr-Ni alloy. The results recommend the optimum conditions of chemical composition and the heat treatment for maximizing the TRIP effect. Comparing to the experimental results, this designing strategy can be utilized to explore the potential materials of the novel alloys.