Artificial Intelligence Inspired Design of Non-Isothermal Heat Treatments for γ - γ' Two-phase Ni-based Alloys

Abstract

Abstract In this paper, a state-of-the-art Artificial Intelligence (AI) technique is used for a precipitation hardenable Ni-based alloy to predict more flexible non-isothermal heat treatment and to examine the possible heat treatment routes for the enhancement in strength that may be practically achieved. Additionally, AI is used to integrate with Materials Integration by Network Technology, which is a computational workflow utilized to model the microstructure evolution and evaluate the 0.2% proof stress for isothermal heat treatment (IHT) and non-isothermal heat treatment (non-IHT). As a result, it is possible to find enhanced 0.2% proof stress for non-IHTs for a fixed time of 10 minutes compared to the IHT benchmark. The entire search space for heat treatment scheduling was ~ 3 billion. Out of 1620 non-IHTs, we succeeded in designing the 110 non-IHTs schedules that outperformed the IHT benchmark. Interestingly, it is found that early-stage high-temperature for a shorter time increases the γ' precipitate size up to the critical size and later heat treatment at lower temperature increases the γ' fraction with no anomalous change in γ' size. Therefore, employing this essence from AI, we designed a heat treatment route in which we attained an outperformed 0.2% proof stress to AI-designed non-IHT routes.