High-throughput map design of creep life in low-alloy steels by integrating machine learning with a genetic algorithm

Abstract

Creep-oriented alloy design is a long-standing interesting topic in the field of metal structural materials. However, the high cost for creep testing limits the development efficiency of new alloys using traditional trial-and-error methods. Additionally, the complex mechanism and influencing factors significantly increase the difficulty of physical modeling and simulation-guided design. In this study, an alloy design framework for creep life improvement is established, including two modules: creep life prediction and high-throughput design. For the first module, based on the creep dataset used in this research, the best machine learning model for creep life prediction was obtained by comparison of various machine learning strategies. By this way, the limitation of complex creep mechanism was partially removed and an accurate and generic model was established. For the second module, a genetic algorithm with a filter was used to obtain promising new alloy plans with optimal composition and processing parameters under specific creep conditions. After proving the rationality of this design framework and related design results, map design was used to guide the new alloy development for different creep conditions, and a new multifunctional alloy plan was proposed. This design system could provide preliminary guidance for high-efficiency alloy designs with complex target properties.