Knowledge-enabled data-driven smart design of ultra-strong ductile near-α titanium alloys under extreme conditions

Abstract

Under extreme service conditions, adiabatic shear banding critically limits the performance of titanium alloys in warhead applications, creating an urgent demand for strategies to achieve strength-ductility synergy. In this work, a knowledge-enabled data-driven multi-objective optimization framework is proposed to investigate the composition of near-α titanium alloys under high strain rates. By integrating domain knowledge with twelve machine learning models, key performance parameters (KPPs) governing strength are identified through feature engineering, including strain rate, Fermi energy, and phase formation parameters, while ductility is controlled by the KPPs of strain rate, bulk/shear modulus (B/G) ratio, and mixing enthalpy. Using a gradient boosting regression tree model for strength prediction [test the coefficient of determination (R²) = 0.91] and a random forest model for ductility prediction (test R² = 0.82), the nondominated sorting genetic algorithm II (NSGA-II) is integrated to identify 14 Pareto-optimal alloys from a pool of 200,000 candidate compositions of near-α titanium alloys (Ti-Al-V-Mo-Zr-Sn system). A breakthrough combination of 1,600 MPa dynamic compressive strength and 26% ductility at a strain rate of 3,000 s^-1 is achieved, which is superior to that of the TA15 alloy, as confirmed by a constitutive equation model. This framework successfully designs novel near α-titanium alloys with strength-ductility synergy through knowledge-driven feature engineering and multi-objective optimization algorithms, establishing a new paradigm for the intelligent design of titanium alloys under extreme conditions.

Keywords

Machine learning / near-α titanium alloy / compress strength / ductility / multi-objective optimization

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