摘要
航空装备材料对安全性与可靠性要求极高,而疲劳性能是其核心性能指标之一。传统的疲劳预测方法依赖大量实验,成本高、周期长,难以满足现代航空工程对高效、精准评估的需求。近年来,机器学习在航空材料疲劳寿命预测中展现出显著潜力。本工作系统综述该领域研究进展,重点涵盖主流模型与建模流程,梳理纯数据驱动方法与融合物理机制方法的核心思路与关键成果,聚焦物理信息嵌入对提升模型精度、可信度与可解释性方面的作用,并评述数据层面、复杂失效机制的信息挖掘不足、模型可解释性与工程应用信任度不足和复杂工况适应性不足的局限性,未来需重点突破构建标准化、高可信度的疲劳数据集,建立面向任务的物理知识自动融合机制,推动面向复杂工况和结构件层级疲劳预测等研究方向。
Aerospace equipment materials demand an ultra-high level of safety and reliability,with fatigue performance being one of their core performance metrics.Traditional fatigue prediction methods rely heavily on extensive experimental tests,which are associated with high costs and long development cycles,thus failing to meet the requirements of modern aerospace engineering for efficient and accurate performance evaluation.In recent years,machine learning has exhibited remarkable potential in the fatigue life prediction of aerospace materials.This work presents a systematic review of the research progress in this field,with a focus on mainstream models and modeling workflows.It clarifies the core ideas and key research findings of both pure data-driven methods and physics-integrated approaches,and centers on the role of physical information embedding in enhancing model accuracy,credibility,and interpretability.Moreover,the paper critically discusses the existing limitations,including insufficient information mining in terms of data dimensions and complex failure mechanisms,inadequate model interpretability and low trustworthiness for engineering applications,as well as poor adaptability to complex service conditions.Finally,key research directions for addressing these limitations are highlighted,such as constructing standardized and highly reliable fatigue datasets,establishing a task-oriented automatic fusion mechanism for physical knowledge,and advancing fatigue life prediction at the level of structural components under complex service conditions.
作者
王璟怡
张悦
钟斌
何玉怀
许巍
WANG Jingyi;ZHANG Yue;ZHONG Bin;HE Yuhuai;XU Wei(AECC Beijing Institute of Aeronautical Materials,Beijing 100095,China;Beijing Key Laboratory of Aerospace Materials Testing and Evaluation,Beijing 100095,China;AECC Key Laboratory of Aerospace Materials Testing and Evaluation,Beijing 100095,China)
出处
《航空材料学报》
北大核心
2026年第3期1-17,共17页
Journal of Aeronautical Materials
基金
稳定支持项目(2019-363)
国家科技重大专项(J2019-Ⅷ-0002-0163)。
关键词
航空材料
疲劳性能
机器学习
数据驱动
物理信息融合
aeronautical material
fatigue property
machine learning
data-driven
physics-informed fusion
