[1]张 闫,薛德祯,辛社伟,等.机器学习辅助钛合金设计应用进展[J].中国材料进展,2025,44(04):319-329.[doi:10.7502/j.issn.1674-3962.202501004]
　ZHANG Yan,XUE Dezhen,XIN Shewei,et al.Research Progress of Machine Learning Aided Titanium Alloys Design[J].MATERIALS CHINA,2025,44(04):319-329.[doi:10.7502/j.issn.1674-3962.202501004]
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机器学习辅助钛合金设计应用进展()
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中国材料进展[ISSN:1674-3962/CN:61-1473/TG]

卷:
44
期数:
2025年04
页码:
319-329
栏目:
出版日期:
2025-04-30

文章信息/Info

Title:
Research Progress of Machine Learning Aided Titanium Alloys Design
文章编号:
1674-3962(2025)04-0319-11
作者:
张 闫; 薛德祯; 辛社伟; 王 晓; 周 伟; 潘 曦; 李星吾; 张冰洁; 郝梦园
1. 西北有色金属研究院，陕西 西安 710016 2. 西安交通大学金属材料强度国家重点实验室，陕西 西安 710049
Author(s):
ZHANG Yan; XUE Dezhen; XIN Shewei; WANG Xiao; ZHOU Wei; PAN Xi;  LI Xingwu; ZHANG Bingjie; HAO Mengyuan
1. Northwest Institute for Nonferrous Metal Research，Xi’an 710016，China 2.State Key Laboratory for Mechanical behavior of Materials，Xi’an Jiaotong University，Xi’an 710049，China
关键词:
钛合金; 机器学习; 合金设计; 特征工程; 数据驱动
Keywords:
titanium alloys;  machine learning;  alloy design;  feature engineering;  data driven
分类号:
TP181;TG146.23
DOI:
10.7502/j.issn.1674-3962.202501004
文献标志码:
A
摘要:
钛合金以其优良的力学性能、生物相容性、耐蚀性及耐热性等特点已成为高性能结构件的首选材料，被广泛应用在医疗器械、化工、航天航空、舰船等领域。随着钛合金中合金化元素种类的进一步增加，钛合金成分、工艺与性能间的映射机制关系愈加复杂，以钼当量、d电子合金理论、价电子浓度等为代表的传统钛合金设计方法很难准确捕捉到合金元素间复杂的交互作用及其对组织和性能的影响规律。近年来，机器学习技术有望从材料数据中通过算法挖掘材料成分、工艺、组织、性能之间的隐藏关系，实现实验过程优化，突破研究人员基于经验和“试错法”高成本、低效率的材料设计瓶颈，为钛合金智能设计开辟了新的思路。以机器学习辅助钛合金设计研究的流程为主线，介绍了机器学习辅助钛合金设计研发中的数据来源与预处理、特征工程、机器学习建模预测和优化设计等技术，综述了数据驱动的智能化研发范式在钛合金设计中的研究进展。最后，分析了这一新型研发范式在钛合金领域面临的问题并展望了其发展前景。
Abstract:
Titanium alloys, known for their excellent mechanical properties,biocompatibility, corrosion resistance, and heat resistance, have become the material of choice for high-performance structural components. They are widely used in fields such as medical devices, chemical engineering, aerospace, and naval ships. As the variety of alloying elements in titanium alloys continues to increase, the mapping relationship between composition, processing, and performance becomes increasingly complex. Traditional design methods for titanium alloys, such as molybdenum equivalence, d-electron alloy theory, and valence electron concentration, struggle to accurately capture the complex interactions between alloying elements and their impact on the microstructure and performance. In recent years, machine learning technologies have shown promise in uncovering hidden relationships between material composition, processing, microstructure, and performance by mining material data through algorithms. This offers the potential to optimize experimental processes and overcome the high cost and inefficiency of trial-and-error methods in material design, opening new avenues for intelligent design of titanium alloys. This paper presents an overview of the machine learning-assisted design process for titanium alloys, including data sourcing and preprocessing, feature engineering, machine learning modeling and prediction, and optimization design. It reviews the research progress of datadriven intelligent design paradigms in titanium alloy development. Finally, the paper analyzes the challenges faced by this new research paradigm in the titanium alloy field and discusses its future prospects.

备注/Memo

备注/Memo:
收稿日期：2025-01-04修回日期：2025-02-09 基金项目：国家自然科学基金重点项目（52431001）；国家自然科学 基金面上项目(5207011470)；陕西省创新能力支撑计划项目（2024ZG-GCZX-01(1)-06）；西北有色金属研究院自开科技项目（0501YK2501） 第一作者：张闫，女，1994年生，工程师 通讯作者：薛德祯，男，1984年生，教授，博士生导师， Email:xuedezhen@xjtu.edu.cn 辛社伟，男，1978年生，教授，博士生导师， Email:nwpu_xsw@126.com
更新日期/Last Update: 2025-03-28