Al、V含量对Ti–Al–V合金组织与性能的影响：第一性原理–机器学习研究

doi:10.11900/0412.1961.2026.00116

金属学报 DOI: 10.11900/0412.1961.2026.00116

曹烁¹, 陈新澳^1,2, 梁弼宁^1,2, 金伟¹, 胡青苗¹

1 中国科学院金属研究所沈阳 110016

2 中国科学技术大学材料科学与工程学院沈阳110016

Effects of Al and V Contents on the Microstructure and Properties of Ti–Al–V Alloys: Insights from First-Principles Calculations and Machine Learning

CAO Shuo ¹, CHEN Xinao ^1,2, LIANG Bining ^1,2, JIN Wei ¹, HU Qingmiao ¹

1 Institute of Metal Research, Chinese Academy of Sciences, Shenyang 110016, China

2 School of Materials Science and Engineering, University of Science and Technology of China, Shenyang 110016, China

引用本文:

曹烁, 陈新澳, 梁弼宁, 金伟, 胡青苗. Al、V含量对Ti–Al–V合金组织与性能的影响：第一性原理–机器学习研究[J]. 金属学报, DOI: 10.11900/0412.1961.2026.00116.

全文: PDF(7811 KB)

摘要: Ti–Al–V系合金广泛应用于航空航天、化工及医疗领域，但现有工程应用主要集中于Ti-6Al-4V和Ti-3Al-2.5V，成分设计空间仍有待拓展。同时，Al、V含量对合金性能的影响规律尚不明确。针对这一问题，本工作构建了数据与知识双驱动的双相钛合金力学性能预测方法。基于第一性原理计算与人工神经网络，建立了Ti–Al–V体系基础物性数据库及“成分—基础物性”代理模型，并系统分析了Al、V含量对α、β两相晶格及弹性性质的影响。结合CALPHAD方法获得相组成及体积分数，并基于强化机制模型预测屈服强度，提出多相间的综合剪切模量作为合金韧性相关的相对评价指标。基于成分—组织—性能关系，提出了Ti–Al–V合金的成分优化建议，为高性能Ti–Al–V合金设计提供了理论依据。

关键词 ： Ti-Al-V、第一原理, 成分设计、机器学习、物性参数, 强韧性

Abstract：Ti-Al-V alloys are widely used in the aerospace, chemical, and medical industries. However, current engineering applications are primarily limited to Ti-6Al-4V and Ti-3Al-2.5V, leaving significant room for expanding the compositional design space. Furthermore, the underlying laws governing the effects of Al and V contents on alloy properties remain to be fully elucidated. To address this issue, this work proposes a data- and knowledge-driven approach for predicting the mechanical properties of dual-phase titanium alloys. Based on first-principles calculations and artificial neural networks, a fundamental physical property database and a "composition–property" surrogate model for the Ti-Al-V system were established, and the effects of Al and V contents on the lattice and elastic properties of the αand βphases were systematically analyzed. By integrating the CALPHAD method to obtain phase compositions and volume fractions, the yield strength was predicted based on strengthening mechanism models, and a comprehensive shear modulus ratio among multiple phases was proposed to evaluate the toughness of the alloys. Based on the composition–microstructure–property relationship, compositional optimization strategies for Ti-Al-V alloys were proposed, providing a theoretical foundation for the design of high-performance Ti-Al-V alloys.

Key words： Ti-Al-V First-principles Composition Design Machine Learning Material Properties Strength and Toughness

收稿日期: 2026-04-27

基金资助:国家重点研发计划(2023YFB3710700)

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