A novel semi-empirical design method for TRIP titanium alloys

To address the limitations of conventional semi-empirical design methods for TRIP titanium alloys, including limited applicability, low accuracy, and inability to guide heat treatment, this study develops a novel Cβ-ΔT diagram. By leveraging machine learning to comprehensively mine data and extract two key parameters—the composite β-phase stability coefficient (Cβ) and heat treatment sensitivity coefficient (ΔT)—this method enables the concurrent design of alloy composition and heat treatment. The Cβ-ΔT diagram significantly improves prediction accuracy from 48.5% of traditional methods to 90.8%, thereby offering an effective tool for developing high-performance TRIP titanium alloys.