基于机器学习的航空材料疲劳寿命预测研究进展

Progress on fatigue life prediction of aeronautical materials based on machine learning

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。

Abstract

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.

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

Key words

aeronautical material / fatigue property / machine learning / data-driven / physics-informed fusion

参考文献

[1] 王国荣. 无损检测在航空装备维修中的应用与发展[J].模具制造，2025，25（4）：234-236.

[2] WANG G R . Application and development of non-destructive testing in aviation equipment maintenance[J].Die & Mould Manufacturing，2025，25（4）：234-236.

[3] 刘巧沐，李园春. 航空发动机材料及工艺发展浅析[J].航空动力，2021（6）：9-12.

[4] LIU Q M， LI Y C . Analysis to development of aero engine materials and processes[J].Journal of Aerospace Power，2021（6）：9-12.

[5] 代定强，周轩，董雷霆. 数字工程与数字孪生在航空疲劳与结构完整性领域的研究进展与展望[J].航空学报，2025，46（19）：19-40.

[6] DAI D Q， ZHOU X， DONG L T . Research progress and prospect of digital engineering and digital twin in field of aeronautical fatigue and structural integrity[J].Acta Aeronautica et Astronautica Sinica，2025，46（19）：19-40.

[7] AVATEFFAZELI M， WEBSTER G， TAHMASBI K，et al. Very high cycle fatigue at elevated temperatures：a review on high temperature ultrasonic fatigue[J].Journal of Space Safety Engineering，2022，9（4）：488-512.

[8] YANG K， HE C， HUANG Q，et al. Very high cycle fatigue behaviors of a turbine engine blade alloy at various stress ratios[J].International Journal of Fatigue，2017，99：35-43.

[9] 陶春虎，习年生，钟培道. 航空装备失效典型案例分析[M]. 北京：国防工业出版社，1998.

[10] TAO C H， XI N S， ZHONG P D . Typical case analysis of aviation equipment failure[M]. Beijing：National Defense Industry Press，1998.

[11] 陈新，许巍，何玉怀. 基于DIC的超高频振动疲劳试样动态全场应变测量方法[J].实验力学，2021，36（5）：677-685.

[12] CHEN X， XU W， HE Y H . Ultra-high frequency vibration fatigue dynamic strain field measurement based on digital image correlation method[J].Journal of Experimental Mechanics，2021，36（5）：677-685.

[13] OH B . The exponential law of endurance tests[C]//Proceeding of the American Society for Testing and Materials. Pennsylvania：American Society for Testing and Materials，1910，10：625-630.

[14] 魏艳娇，向清怡. 基于机器学习的焊接接头疲劳寿命预测方法综述[J].兵器材料科学与工程，2025，48（3）：183-190.

[15] WEI Y J， XIANG Q Y . Review of machine learning-based fatigue life prediction methods for welded joints[J].Ordnance Material Science and Engineering，2025，48（3）：183-190.

[16] 杨晓光，谭龙，郝文琦，等. 数据驱动的高温结构强度与寿命评估：进展与挑战[J].推进技术，2023，44（5）：1-18.

[17] YANG X G， TAN L， HAO W Q，et al. Data-driven structural strength and life assessment of high temperature structure：progresses and challenges[J].Journal of Propulsion Technology，2023，44（5）：1-18.

[18] 胡雅楠，余欢，吴圣川，等. 基于机器学习的增材制造合金材料力学性能预测研究进展与挑战[J].力学学报，2024，56（7）：1892-1915.

[19] HU Y N， YU H， WU S C，et al. Machine learned mechanical properties prediction of additively manufactured metallic alloys：progress and challenges[J].Chinese Journal of Theoretical and Applied Mechanics，2024，56（7）：1892-1915.

[20] 牛昌海，孙倩，郑佳，等. 入水温度和时效参数对7075铝合金析出和力学性能的影响[J].材料工程，2025，53（4）：35-42.

[21] NIU C H， SUN Q， ZHENG J，et al. Effect of water entry temperature and aging parameters on precipitation and mechanical properties of 7075 aluminum alloy[J].Journal of Materials Engineering，2025，53（4）：35-42.

[22] 祝亚亮，雍维，杨杰，等. 热力学计算与机器学习相结合逆向设计镍基高温合金[J].材料工程，2024，52（6）：167-176.

[23] ZHU Y L， YONG W， YANG J，et al. Reverse design of nickel-based superalloys based on thermodynamic calculation and machine learning[J].Journal of Materials Engineering，2024，52（6）：167-176.

[24] 高威，周希晨，朱前勇，等. 面向极端载荷环境用高熵合金的研究进展[J].航空材料学报，2024，44（5）：154-173.

[25] GAO W， ZHOU X C， ZHU Q Y，et al. Research progress on high-entropy alloys for extreme loading environments[J].Journal of Aeronautical Materials，2024，44（5）：154-173.

[26] 李桌汉，有移亮，赵子华，等. 人工智能技术在失效分析领域的应用[J].航空材料学报，2024，44（5）：1-16.

[27] LI Z H， YOU Y L， ZHAO Z H，et al. Application of artificial intelligence technology in failure analysis[J].Journal of Aeronautical Materials，2024，44（5）：1-16.

[28] 高田创，高建宝，李谦，等. 机器学习驱动难熔高熵合金设计的现状与展望[J].材料工程，2024，52（1）：27-44.

[29] GAO T C， GAO J B， LI Q，et al. Current status and prospects in machine learning-driven design for refractory high-entropy alloys[J].Journal of Materials Engineering，2024，52（1）：27-44.

[30] 甘磊，吴昊，仲政. 数据驱动的金属疲劳寿命模型研究进展[J].力学进展，2025，55（1）：30-79.

[31] GAN L， WU H， ZHONG Z . Advances in data-driven models for fatigue life prediction of metallic materials[J].Advances in Mechanics，2025，55（1）：30-79.

[32] CHEN S N， BAI Y T， ZHOU X H，et al. A deep learning dataset for metal multiaxial fatigue life prediction[J].Scientific Data，2024，11：1027.

[33] 蔡培，袁辉，徐鹤鸣，等. 基于小样本量的航空发动机材料高低周复合疲劳P-S-N曲线优化方法[J].材料工程，2024，52（10）：117-126.

[34] CAI P， YUAN H， XU H M，et al. Optimization method of combined high and low cycle fatigueP-S-N curve for aeroengine materials with small size sample[J].Journal of Materials Engineering，2024，52（10）：117-126.

[35] 张聪，刘杰，解树一，等. 高通量计算与机器学习驱动高熵合金的研究进展[J].材料工程，2023，51（3）：1-16.

[36] ZHANG C， LIU J， XIE S Y，et al. Research progress in high-entropy alloys driven by high throughput computation and machine learning[J].Journal of Materials Engineering，2023，51（3）：1-16.

[37] MIZUNO Y， HOSOI A， KOSHITA H，et al. Fatigue life prediction of composite materials using strain distribution images and a deep convolution neural network[J].Scientific Reports，2024，14（1）：25418.

[38] HORŇAS J， BĚHAL J， HOMOLA P，et al. Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach[J].International Journal of Fatigue，2023，169：107483.

[39] PARMAR A， KATARIYA R， PATEL V . A review on random forest：an ensemble classifier[C]//International Conference on Intelligent Data Communication Technologies and Internet of Things. Singapore：Springer Singapore，2018：758-763.

[40] GUO J Y， ZAN X P， WANG L，et al. A random forest regression with Bayesian optimization-based method for fatigue strength prediction of ferrous alloys[J].Engineering Fracture Mechanics，2023，293：109714.

[41] CHEN T . XGBoost：a scalable tree boosting system[D]. New York：Cornell University，2016.

[42] CHEN T， HE T， BENESTY M，et al. XGBoost：extreme gradient boosting[J].R Package Version，2015，1（4）：1-4.

[43] JIANG L F， HU Y N， LIU Y X，et al. Physics-informed machine learning for low-cycle fatigue life prediction of 316 stainless steels[J].International Journal of Fatigue，2024，182：108187.

[44] GOODFELLOW I， BENGIO Y， COURVILLE A . Deep learning[M]. Massachusetts：The MIT Press，2016.

[45] YANG J Y， KANG G Z， LIU Y J，et al. A novel method of multiaxial fatigue life prediction based on deep learning[J].International Journal of Fatigue，2021，151：106356.

[46] MCCULLOCH W S， PITTS W . A logical calculus of the ideas immanent in nervous activity[J].The Bulletin of Mathematical Biophysics，1943，5（4）：115-133.

[47] HAN J， MORAGA C . The influence of the sigmoid function parameters on the speed of backpropagation learning[C]//International Workshop on Artificial Neural Networks. Berlin：Springer-Verlag，1995：195-201.

[48] YIN X， GOUDRIAAN J A N， LANTINGA E A，et al. A flexible sigmoid function of determinate growth[J].Annals of Botany，2003，91（3）：361-371.

[49] CHEN Y P， DAI X Y， LIU M C，et al. Dynamic relu[C]//European Conference on Computer Vision. Cham：Springer International Publishing Switzerland/Springer Nature Switzerland AG，2020：351-367.

[50] CHEN J， LIU Y M . Probabilistic physics-guided machine learning for fatigue data analysis[J].Expert Systems with Applications，2021，168：114316.

[51] ZHANG X C， GONG J G， XUAN F Z . A deep learning based life prediction method for components under creep，fatigue and creep-fatigue conditions[J].International Journal of Fatigue，2021，148：106236.

[52] WANG H J， LI B， GONG J G，et al. Machine learning-based fatigue life prediction of metal materials：perspectives of physics-informed and data-driven hybrid methods[J].Engineering Fracture Mechanics，2023，284：109242.

[53] ZHAN Z X， HU W P， MENG Q C . Data-driven fatigue life prediction in additive manufactured titanium alloy：a damage mechanics based machine learning framework[J].Engineering Fracture Mechanics，2021，252：107850.

[54] BERGSTRA J， BENGIO Y . Random search for hyper-parameter optimization[J].The Journal of Machine Learning Research，2012，13（1）：281-305.

[55] JAMES G， WITTEN D， HASTIE T，et al. An introduction to statistical learning[M]. New York：Springer-Verlag New York，Inc.，2013.

[56] JIA Y F， FU R， LING C，et al. Fatigue life prediction based on a deep learning method for Ti-6Al-4V fabricated by laser powder bed fusion up to very-high-cycle fatigue regime[J].International Journal of Fatigue，2023，172：107645.

[57] ZHU S Y， ZHANG Y， CHEN X，et al. A multi-algorithm integration machine learning approach for high cycle fatigue prediction of a titanium alloy in aero-engine[J].Engineering Fracture Mechanics，2023，289：109485.

[58] LIU X L， ZHANG S Y， CONG T，et al. Very high-cycle fatigue life prediction of high-strength steel based on machine learning[J].Fatigue & Fracture of Engineering Materials & Structures，2024，47（3）：1024-1035.

[59] SHI T， SUN J Y， LI J H，et al. Machine learning based very-high-cycle fatigue life prediction of AlSi10Mg alloy fabricated by selective laser melting[J].International Journal of Fatigue，2023，171：107585.

[60] ZHU S Y， ZHANG Y， ZHU B C，et al. High cycle fatigue life prediction of titanium alloys based on a novel deep learning approach[J].International Journal of Fatigue，2024，182：108206.

[61] SRINIVASAN D V， MORADI M， KOMNINOS P，et al. A generalized machine learning framework to estimate fatigue life across materials with minimal data[J].Materials & Design，2024，246：113355.

[62] QIAN H J， HUANG Z Y， XU Y T，et al. Very high cycle fatigue life prediction of Ti60 alloy based on machine learning with data enhancement[J].Engineering Fracture Mechanics，2023，289：109431.

[63] LIU Y K， FAN J L， ZHU G，et al. Data-driven approach to very high cycle fatigue life prediction[J].Engineering Fracture Mechanics，2023，292：109630.

[64] LEI L M， LI B， WANG H J，et al. High-temperature high-cycle fatigue performance and machine learning-based fatigue life prediction of additively manufactured Hastelloy X[J].International Journal of Fatigue，2024，178：108012.

[65] SONG Z X， PENG J L， ZHU L N，et al. High-cycle fatigue life prediction of additive manufacturing Inconel 718 alloy via machine learning[J].Materials，2025，18（11）：2604.

[66] ZHANG M， SUN C N， ZHANG X，et al. High cycle fatigue life prediction of laser additive manufactured stainless steel：a machine learning approach[J].International Journal of Fatigue，2019，128：105194.

[67] 谢沛东，谢德巧，周凯，等. 基于疲劳源孔隙的增材制造Ti6Al4V疲劳寿命预测[J].激光与光电子学进展，2024，61（21）：179-187.

[68] XIE P D， XIE D Q， ZHOU K，et al. Fatigue life prediction of additive manufacturing Ti6Al4V based on fatigue source pore[J].Laser & Optoelectronics Progress，2024，61（21）：179-187.

[69] OWHADI H . Bayesian numerical homogenization[J].Multiscale Modeling & Simulation，2015，13（3）：812-828.

[70] RAISSI M . Deep hidden physics models：deep learning of nonlinear partial differential equations[J].Journal of Machine Learning Research，2018，19（25）：1-24.

[71] RAISSI M， PERDIKARIS P， KARNIADAKIS G E . Physics-informed neural networks：a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J].Journal of Computational Physics，2019，378：686-707.

[72] ABIRIA I， WANG C， ZHANG Q C，et al. High-cycle and very-high-cycle fatigue life prediction in additive manufacturing using hybrid physics-informed neural networks[J].Engineering Fracture Mechanics，2025，319：111026.

[73] HAO W Q， TAN L， YANG X G，et al. A physics-informed machine learning approach for notch fatigue evaluation of alloys used in aerospace[J].International Journal of Fatigue，2023，170：107536.

[74] ZHANG X C， GONG J G， XUAN F Z . A physics-informed neural network for creep-fatigue life prediction of components at elevated temperatures[J].Engineering Fracture Mechanics，2021，258：108130.

[75] TAMURA M， ABE F， SHIBA K，et al. Larson-miller constant of heat-resistant steel[J].Metallurgical and Materials Transactions A，2013，44（6）：2645-2661.

[76] ZHU M L， XUAN F Z， DU Y N，et al. Very high cycle fatigue behavior of a low strength welded joint at moderate temperature[J].International Journal of Fatigue，2012，40：74-83.

[77] ZHU M L， JIN L， XUAN F Z . Fatigue life and mechanistic modeling of interior micro-defect induced cracking in high cycle and very high cycle regimes[J].Acta Materialia，2018，157：259-275.

[78] FAN J L， ZHU G， ZHU M L，et al. A data-physics integrated approach to life prediction in very high cycle fatigue regime[J].International Journal of Fatigue，2023，176：107917.

[79] CHEN J， LIU Y M . Fatigue property prediction of additively manufactured Ti-6Al-4V using probabilistic physics-guided learning[J].Additive Manufacturing，2021，39：101876.

[80] ZHOU T G， SUN X Y， CHEN X . A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading[J].Philosophical Transactions of the Royal Society A，2023，381（2260）：20220392.

[81] ACHARYA R， CAPUTO A N， NEU R W . Predicting creep-fatigue and thermomechanical fatigue life of Ni-base superalloys using a probabilistic physics-guided neural network[J].Fatigue & Fracture of Engineering Materials & Structures，2023，46（4）：1554-1571.

[82] ACHARYA R， CAPUTO A N， NEU R W . Machine learning model for predicting the influence of crystallographic orientation on thermomechanical fatigue of Ni-base superalloys[J].International Journal of Fatigue，2025，194：108832.

[83] CIAMPAGLIA A， TRIDELLO A， PAOLINO D S，et al. Data driven method for predicting the effect of process parameters on the fatigue response of additive manufactured AlSi10Mg parts[J].International Journal of Fatigue，2023，170：107500.

[84] CHEN D， LI Y Z， LIU K，et al. A physics-informed neural network approach to fatigue life prediction using small quantity of samples[J].International Journal of Fatigue，2023，166：107270.

[85] DONG Y X， YANG X F， CHANG D D，et al. Predicting fatigue life of multi-defect materials using the fracture mechanics-based physics-informed neural network framework[J].International Journal of Fatigue，2025，190：108626.

[86] FENG F， ZHU T， YANG B，et al. Probabilistic fatigue life prediction in additive manufacturing materials with a physics-informed neural network framework[J].Expert Systems with Applications，2025，275：127098.

[87] SCHIJVE J . Fatigue of structures and materials[M]. Dordrecht：Springer Netherlands，2009.

[88] LIAO W W， LONG X Y， JIANG C . A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth[J].International Journal of Fatigue，2025，191：108678.

[89] PARIS P， ERDOGAN F . A critical analysis of crack propagation laws[J].Journal of Basic Engineering，1963，85（4）：528-533.

[90] LIU H H， DING X， LIU J H，et al. A Physics-informed neural network model for combined high and low cycle fatigue life prediction[J].Mechanics of Materials，2025，209：105429.

[91] MINER M A . Cumulative damage in fatigue[J].Journal of Applied Mechanics，1945，12（3）：A159-A164.

[92] WU Z K， WU S C， KRUZIC J J，et al. Critical damage events of 3D printed AlSi10Mg alloy via in situ synchrotron X-ray tomography[J].Acta Materialia，2025，282：120464.

[93] HUANG T H， LI X Y， ZHANG Y Z，et al. Multifactorial prediction of corrosion fatigue crack growth in aluminum alloys using physics-informed neural networks[J].Engineering Failure Analysis，2025，174：109521.

[94] WALKER K . The effect of stress ratio on fatigue crack growth[C]//Fatigue Crack Growth Under Spectrum Loads. Philadelphia：American Society for Testing and Materials，1970：225-233.

[95] XU P， JI X， LI M，et al. Small data machine learning in materials science[J].npj Computational Materials，2023，9（1）：42.

[96] BOSTANABAD R， CHAN Y， AHMED S，et al. Computational microstructure characterization and reconstruction for data-driven materials science：a review[J].Progress in Materials Science，2022，129：100952.

[97] ZHENG X， LIU J， LUO J，et al. Environment-assisted fatigue and fracture of metallic materials：mechanisms，modeling，and data challenges[J].Engineering Fracture Mechanics，2023，289：109456.

基金资助

稳定支持项目(2019-363)

国家科技重大专项(J2019-Ⅷ-0002-0163)