An integrated design of novel RAFM steels with targeted microstructures and tensile properties using machine learning and CALPHAD

Abstract

The design optimization of structure and performance of reduced activation ferritic-martensitic (RAFM) steels is crucial for the development of future fusion reactors, which has always been a significant challenge. In this study, we proposed a new strategy to integrate the microstructure and performance design of RAFM steels using machine learning (ML) and calculation of phase diagrams (CALPHAD). Since the microstructures (MX, M₂₃C₆, δ-ferrite, coarsening phases, etc.) play important roles in mechanical properties of RAFM steels, a microstructural model was built by ML to predict their volume fraction or presence based on the CALPHAD data. By integrating this microstructural model with the forward and reverse models, we developed two RAFM steels with high volume fraction of MX (0.49% and 0.42%) and excellent tensile properties. At 600 °C, the ultimate tensile strength (UTS) of the two RAFM steels is about 100 MPa higher than that of the conventional RAFM steels. These experimental results meet the specific design criteria, confirming the effectiveness of our design strategy. Our research will provide a valuable guideline for the design of other advanced alloys.

Keywords

Machine learning / integrated design / RAFM steels / tensile properties / MX precipitates

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