AI-Driven Aging Pattern Analysis for Improving 0.2% Proof Stress in Binary Ni-Al Alloys with γ – γ' Two-Phase Structure

Abstract

Abstract This study presents the comprehensive analysis of flexible non-isothermal aging (NIA) patterns discovered through artificial intelligence (AI) to improve the mechanical strength (0.2% proof stress) in γ – γ' two-phase, binary Ni-Al alloys. In our recent investigation, we found that the AI algorithm could propose aging patterns with superior strength compared to conventional isothermal aging. In this current study, we continued our extensive exploration of AI methodologies, uncovering diverse patterns that also surpassed the isothermal aging benchmark. Remarkably, out of 2823 NIA schedules, we found 173 ones outperforming the isothermal aging benchmark. Furthermore, we conducted a detailed analysis of newly AI-discovered patterns. We identified two critical factors for strength improvement: exposure at 700 ℃ and the number of consecutive 700 ℃ exposures (optimally set at two), alongside non-consecutive steps (up to five). The insights gained from these findings may demonstrate the potential of AI-driven approaches to yield ideas on how to achieve improved strength in Ni-Al alloys.