数字孪生技术在材料服役评价中的应用

Application of digital twin technology in material service evaluation

摘要

材料服役评价涵盖了材料性能表征、失效分析、剩余寿命预测等多个方面，对于保障重大工程装备的安全服役和维保策略优化意义重大. 随着材料性能的提升和服役环境的日益复杂化，传统的材料服役评价方法在实时性、精准性和智能化等方面存在一定的局限性. 数字孪生作为融合物理模型、数据驱动模型与实时监测的前沿技术，为材料服役状态的实时动态感知与服役性能精准预测提供了新的解决方案. 在材料服役过程中，腐蚀、疲劳、断裂和磨损是四种最典型的失效形式，一旦发生将直接影响重大工程与装备的服役安全. 本文首先系统综述了数字孪生技术在针对上述四种典型失效形式的服役评价研究中的应用进展. 随后，深入分析了用于材料服役评价的多源数据融合、多尺度建模、实时数据传输、服役评价模型构建等数字孪生关键技术的研究现状. 最后，对用于材料服役评价的数字孪生技术存在的问题及未来发展趋势进行了展望.

Abstract

Material service evaluation is crucial to ensure the safety of major engineering equipment and optimize maintenance strategies. It encompasses performance characterization, failure analysis, and the prediction of the remaining life. However, as service environments in fields, such as aerospace, nuclear energy, and transportation, have become increasingly complex, traditional evaluation methods have limitations. These conventional approaches, which often rely on periodic offline testing and empirical formulas, struggle to meet the modern demands for real-time monitoring, high-fidelity accuracy, and intelligent decision-making. Digital twin technology has emerged as a transformative solution to these problems. By integrating high-fidelity physical models, data-driven approaches, and real-time sensing data, a digital twin can enable dynamic bidirectional mapping between a physical entity and its virtual counterpart. This technology enables the continuous perception of material states and the precise prediction of service performance throughout the lifecycle. In terms of material degradation, the four most prevalent and critical failure modes are corrosion, fatigue, fracture, and wear. However, their occurrence directly threatens the operational safety of large-scale structures. This paper provides a systematic review of recent progress in the application of digital twin technology to evaluate these four typical failure modes. For corrosion evaluation, this study highlights how a digital twin integrates environmental sensor data with electrochemical models to transition from offline analysis to real-time rate prediction. Recent studies on fatigue and fracture have emphasized the integration of multiscale simulation techniques. These approaches combine microscopic crystal plasticity modeling with macroscopic finite element analysis. Consequently, the accumulation of fatigue damage and the propagation behavior of cracks can be tracked with improved reliability and accuracy. For wear evaluation, digital twin technology is increasingly incorporating machine-learning and transfer-learning techniques. These methods can identify the degradation patterns in tools, gears, and other mechanical components under varying operational conditions. Such approaches significantly enhance the adaptability and predictive capability of wear-monitoring systems. In addition to reviewing failure-mode applications, this paper provides a comprehensive discussion of the key enabling technologies required to construct an effective digital twin framework for material service evaluation. Multisource heterogeneous data fusion was identified as the fundamental basis for integrating the experimental measurements, operational monitoring data, and simulation outputs. Multiscale modeling is an essential approach for connecting microscopic damage mechanisms to macroscopic structural responses. Furthermore, real-time data transmission and edge computing technologies were examined. These technologies support low-latency communication and ensure synchronization between physical systems and their digital counterparts. Despite considerable progress in recent years, several technical challenges remain. The integration of high-dimensional heterogeneous data requires further methodological developments. Multiscale simulations often involve high computational costs, which limits their real-time application potential. Additionally, balancing model complexity with real-time performance remains a significant issue in practical engineering deployment. Finally, future development directions for digital twin technology in material service evaluation are discussed. Emerging cloud, edge, and terminal collaborative architectures are expected to enhance computational efficiency and data-processing capability. The integration of uncertainty quantification methods is also emphasized, as it enables probabilistic risk assessment and improves decision reliability. These developments will further promote intelligent and reliable material service evaluation in complex engineering systems.

关键词

数字孪生 / 材料服役 / 材料失效 / 性能预测 / 数据融合 / 多尺度建模

Key words

digital twin / material service / material failure / performance prediction / data fusion / multiscale modeling

参考文献

[1] Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development[J]. Acta Metall Sin, 2021, 57（11）: 1343

[2] （谢建新, 宿彦京, 薛德祯, 等. 机器学习在材料研发中的应用[J]. 金属学报, 2021, 57（11）： 1343）

[3] Li S, Brennan F. Digital twin enabled structural integrity management: Critical review and framework development[J]. Proc Inst Mech Eng Part M J Eng Marit Environ, 2024, 238（4）: 707

[4] Kalidindi S R, Buzzy M, Boyce B L, et al. Digital twins for materials[J]. Front Mater, 2022, 9: 818535

[5] D’Amico D, Ekoyuncu J, Addepalli S, et al. Conceptual framework of a digital twin to evaluate the degradation status of complex engineering systems[J]. Procedia CIRP, 2019, 86: 61

[6] Maddikunta P K R, Pham Q V, Prabadevi B, et al. Industry 5.0: A survey on enabling technologies and potential applications[J]. J Ind Inf Integr, 2022, 26: 100257

[7] Zhou Y B, Li M, Wang S K, et al. Composites digital twins and their applications in manufacturing process[J]. Fiber Compos, 2023, 40（3）: 76

[8] （周钰博, 李敏, 王绍凯, 等. 复合材料数字孪生及其在制造过程中的应用[J]. 纤维复合材料, 2023, 40（3）： 76）

[9] Stavropoulos P, Papacharalampopoulos A, Athanasopoulou L. A molecular dynamics based digital twin for ultrafast laser material removal processes[J]. Int J Adv Manuf Technol, 2020, 108（1—2）: 413

[10] Papadimitriou I, Gialampoukidis I, Vrochidis S, et al. AI methods in materials design, discovery and manufacturing: A review[J]. Comput Mater Sci, 2024, 235: 112793

[11] Wang C H, Zhou X D, Zhang Y Q, et al. Real—time data sensing and digital twin model development for pavement material mixing: enhancing workability and optimization[J]. Int J Pavement Eng. 2024, 25（1）: 2417973

[12] Wang Y C, Tao F, Zuo Y, et al. Digital—twin—enhanced quality prediction for the composite materials[J]. Engineering, 2023, 22: 23

[13] Hong J C, Ma S K, Liang F W, et al. System integration and intelligent management of hydrogen fuel cells based on digital twin technology[J]. Chin J Eng, 2025, 47（11）: 2296

[14] （洪吉超, 马世琨, 梁峰伟, 等. 氢燃料电池数字孪生技术的系统集成与智能管理[J]. 工程科学学报, 2025, 47（11）： 2296）

[15] Meng D Z, Yang W D, Cai Z （H/X）, et al. Review of digital twin methods for additive manufacturing[J]. Comput Integr Manuf Syst, 2024, 30（4）: 1171

[16] （孟德状, 杨伟东, 蔡子行, 等. 面向增材制造的数字孪生实施方法综述[J]. 计算机集成制造系统, 2024, 30（4）： 1171）

[17] Xiang F, Zhang Z, Zuo Y, et al. Digital twin driven green material optimal—selection towards sustainable manufacturing[J]. Procedia CIRP, 2019, 81: 1290

[18] Shi L Z, Ding Y, Cheng B. Development and application of digital twin technique in steel structures[J]. Appl Sci, 2024, 14（24） : 11685

[19] Taheri H, Gonzalez Bocanegra M, Taheri M. Artificial intelligence, machine learning and smart technologies for nondestructive evaluation[J]. Sensors, 2022, 22（11）: 4055

[20] Yi W W, Li J S, Liu W M, et al. Integrated computational materials engineering for advanced materials: A brief review[J]. Comput Mater Sci, 2019, 158: 42

[21] Tuegel E J, Ingraffea A R, Eason T G, et al. Reengineering aircraft structural life prediction using a digital twin[J]. Int J Aerosp Eng, 2011, 2011: 154798

[22] Hochhalter J D, Leser W P, Newman J A, et al. Coupling damage—sensing particles to the digital twin concept[R/OL]. 2014—05—28. https://ntrs.nasa.gov/api/citations/20140006408/downloads/20140006408.pdf

[23] Liang N M. Digital Twins in Action towards Model Based Enterprise[M]. Beijing: China Machine Press, 2020

[24] （梁乃明. 数字孪生实战——基于模型的数字化企业（MBE）[M]. 北京：机械工业出版社, 2020）

[25] Schluse M, Rossmann J. From simulation to experimentable digital twins: Simulation—based development and operation of complex technical systems[C]// 2016 IEEE International Symposium on Systems Engineering （ISSE）, 2016: 1

[26] Franz J K, Shannon F, Anabela A. Transdisciplinary Perspectives on Complex Systems: New Findings and Approaches[M]. Cham: Springer International Publishing, 2017

[27] Tao F, Liu W R, Liu J H, et al. Digital twin and its potential application exploration[J]. Comput Integr Manuf Syst, 2018, 24（1）: 1

[28] （陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24（1）： 1）

[29] Madni A M, Madni C C, Lucero S D. Leveraging digital twin technology in model—based systems engineering[J]. Systems, 2019, 7（1）: 7

[30] Li H, Wang H Q, Cheng Y, et al. Technology and application of data—driven intelligent services for complex products[J]. China Mech Eng, 2020, 31（7）: 757

[31] （李浩, 王昊琪, 程颖, 等. 数据驱动的复杂产品智能服务技术与应用[J]. 中国机械工程, 2020, 31（7）： 757）

[32] Grieves M. Digital Twin: Manufacturing Excellence through Virtual Factory Replication[J]. White paper, 2014: 1

[33] Tao F, Zhang M. Digital Twin Shop—Floor: A New Shop—Floor Paradigm Towards Smart Manufacturing[J]. IEEE Access, 2017, 5: 20418

[34] Ayyub B, Stambaugh K. Infrastructure lifecycle corrosion management using AI analytics and digital twins[J]. Corros Mater Degrad, 2025, 6（2）: 18

[35] Li H F, Liu Y, Chen J R, et al. Design and Realization of hybrid digital twin based on carbon steel corrosion[J]. Chin J Ship Res, 2021, 16（5）: 230

[36] （李华峰, 刘炎, 陈建如, 等. 基于碳钢腐蚀的混合数字孪生的设计与实现[J]. 中国舰船研究, 2021, 16（5）： 230）

[37] Ghasemzadeh M, Mokhtari M, Bilgin M H, et al. Pitting corrosion identification approach based on inverse finite element method for marine structure applications[J]. Ocean Eng, 2023, 273: 113953

[38] Kennedy L S N, Raul D, Edgard B M, et al. A digital twin to predict failure probability of an FPSO hull based on corrosion models[J]. J Mar Sci Technol, 2023, 28（4）: 862

[39] Momber A W, Wilms M, Brün D. The use of meteorological and oceanographic sensor data in the German offshore territory for the corrosion monitoring of marine structures[J]. Ocean Eng, 2022, 257: 110994

[40] Tamhane D, Banerjee S, Tallur S. Monitoring corrosion in sacrificial anodes with pulsed eddy current and electromechanical impedance: A comparative analysis[J]. IEEE Sens J, 2022, 22（8）: 8147

[41] Bhowmik S. Digital twin for offshore pipeline corrosion monitoring: A deep learning approach[C]// Offshore Technology Conference, 2021: D041S049R003

[42] Chen S, Xu H, Zhao S Y, et al. Fatigue crack prediction based on distributed optical fiber sensing data and dynamic Bayesian network[J]. Measurement, 2025, 255: 118070

[43] Cheok E W W, Qian X D, Kaps A, et al. A strain—interfaced digital twin solution for corner fatigue crack growth using Bayesian inference[J]. Int J Fatigue, 2025, 191: 108705

[44] He M Y, Wang M H, Wu D, et al. CFRP composite fatigue damage pattern recognition using ultrasonic guided waves[J]. Compos Struct, 2025, 371: 119459

[45] Gu W, Zhang W Y, Wang H. Digital twin virtual entity modeling for rolling bearing fatigue failure[J]. Mach Tool Hydraul, 2023, 51（3）: 193

[46] （顾伟, 张文远, 王恒. 滚动轴承疲劳失效故障的数字孪生虚拟实体建模[J]. 机床与液压, 2023, 51（3）： 193）

[47] Qu H. Research on Digital Modeling and Twinning Methods for Coiled Tubing Considering Material Damage Characteristics [D]. Xi’an: Xi’an Shiyou University, 2023

[48] （渠昊. 考虑材料损伤特性的连续油管数字化建模及其孪生方法研究[D]. 西安：西安石油大学, 2023）

[49] Kong C. Study on Residual Fatigue Life Model of High Pressure Tubing for Digital Twinning [D]. Beijing: China Ship Research and Development Academy, 2023

[50] （孔晨. 面向数字孪生的高压油管剩余疲劳寿命模型研究[D]. 北京：中国舰船研究院, 2023）

[51] Wu B, Wei Q Q, Li X, et al. A four—dimensional digital twin framework for fatigue damage assessment of semi—submersible platforms and practical application[J]. Ocean Eng, 2024, 301: 117273

[52] Wen C K, Liu Z Y, Wu G W, et al. Digital twin—driven fatigue life prediction framework of mechanical structures using a power density theory: Application to off—road vehicle front axle housing[J]. Measurement, 2023, 220: 113352

[53] Jiang F, Ding Y L, Song Y S, et al. Digital Twin—driven framework for fatigue lifecycle management of steel bridges[J]. Struct Infrastruct Eng, 2023, 19（12）: 1826

[54] Smolnicki M, Duda S. Determination of interface fracture parameters in thermoplastic fiber metal laminates under mixed—mode I+II[J]. Polymers, 2025, 17（11）: 1462

[55] Fu S H, Wan Z P, Lu W F, et al. High—accuracy virtual testing of air conditioner’s digital twin focusing on key material’s deformation and fracture behavior prediction[J]. Sci Rep, 2022, 12: 12432

[56] Song J H, Lim S H, Kim K G, et al. Digital—twin—driven diagnostics of crack propagation in a single LiNi_0.7Mn_0.15Co_0.15O₂ secondary particle during lithium intercalation[J]. Adv Energy Mater, 2023, 13（23）: 2204328

[57] Jiang Z T, Gao J Y, Lei M K. Measurement of fracture toughness of TiAlN thin film by micro indentation based on digital twin[J]. China Surf Eng, 2022, 35（5）: 172

[58] （蒋智韬, 高剑英, 雷明凯. 微米压痕测量 TiAlN 薄膜断裂韧性的数字孪生方法[J]. 中国表面工程, 2022, 35（5）： 172）

[59] Fang X, Wang H H, Li W J, et al. Fatigue crack growth prediction method for offshore platform based on digital twin[J]. Ocean Eng, 2022, 244: 110320

[60] Kim W, Lee G, Son H, et al. Estimation of fatigue crack initiation and growth in engineering product development using a digital twin approach[J]. Reliab Eng Syst Saf, 2022, 226: 108721

[61] Li X, Li G J, Bi Z M. Digital twins to predict crack propagation of sustainable engineering materials under different loads[J]. Machines, 2024, 12（2）: 125

[62] Xu W G, Wang Z Y, Zhou Z, et al. Wear state assessment of external gear pump based on system—level hybrid digital twin[J]. Mech Syst Signal Process, 2024, 209: 111123

[63] Song Q H, Peng Y Z, Wang R Q, et al. Tool wear state identification method of thin—walled parts milling process driven by digital twin[J]. Aeronaut Manuf Technol, 2023, 66（3）: 46

[64] （宋清华, 彭业振, 王润琼, 等. 数字孪生驱动的薄壁件铣削刀具磨损状态识别方法[J]. 航空制造技术, 2023, 66（3）： 46）

[65] Feng K, Ji J C, Zhang Y C, et al. Digital twin—driven intelligent assessment of gear surface degradation[J]. Mech Syst Signal Process, 2023, 186: 109896

[66] Zhuang K J, Shi Z C, Sun Y B, et al. Digital twin—driven tool wear monitoring and predicting method for the turning process[J]. Symmetry, 2021, 13（8）: 1438

[67] Matouš K, Geers M G D, Kouznetsova V G, et al. A review of predictive nonlinear theories for multiscale modeling of heterogeneous materials[J]. J Comput Phys, 2017, 330: 192

[68] Hassani S, Dackermann U, Mousavi M, et al. A systematic review of data fusion techniques for optimized structural health monitoring[J]. Inf Fusion, 2024, 103: 102136

[69] Zhou J R, Hong X, Jin P Q. Information fusion for multi—source material data: Progress and challenges[J]. Appl Sci, 2019, 9（17）: 3473

[70] Li F C, Li H G, Qiu J X, et al. Guided wave propagation in H—beam and probability—based damage localization: Guided Wave Propagation in H—Beam and Damage Localization[J]. Struct Control Health Monit, 2017, 24（5）: e1916

[71] Ding Y J, Yao X F, Wang S L, et al. Structural damage assessment using improved Dempster—Shafer data fusion algorithm[J]. Earthq Eng Eng Vib, 2019, 18（2）: 395

[72] Zhou Q F, Zhou H, Zhou Q Q, et al. Structural damage detection based on posteriori probability support vector machine and Dempster—Shafer evidence theory[J]. Appl Soft Comput, 2015, 36: 368

[73] Wang J X, Wang D Z, Wang S H, et al. Fault diagnosis of bearings based on multi—sensor information fusion and 2D convolutional neural network[J]. IEEE Access, 2021, 9: 23717

[74] Kannan V, Dao D V, Li H Z. An information fusion approach for increased reliability of condition monitoring With homogeneous and heterogeneous sensor systems[J]. Struct Health Monit, 2023, 22（3）: 1601

[75] Singh S K, Sikdar S, Malinowski P H. An optimized data fusion strategy for structural damage assessment using electromechanical impedance[J]. Smart Mater Struct, 2021, 30（3）: 035012

[76] Zhou Y H, Shu X S, Bao T F, et al. Dam safety assessment through data—level anomaly detection and information fusion[J]. Struct Health Monit, 2023, 22（3）: 2002

[77] Zhu X S. Research on Integrity Evaluation Method of Gathering and Transportation Pipelines Based on Multi—Source Data Fusion [D]. Beijing: China University of Petroleum （Beijing）, 2022

[78] （朱小松. 基于多源数据融合的集输管道完整性评价方法研究[D]. 北京：中国石油大学（北京）, 2022）

[79] Yuan B, Qiang H F, Wang X R, et al. Review of multi—scale mechanical behavior research on composite solid propellants based on data—driven approach[J]. Propellants Explo Pyrotec, 2024, 49（5）: e202300287

[80] Liu Z L, Wu C T, Koishi M. A deep material network for multiscale topology learning and accelerated nonlinear modeling of heterogeneous materials[J]. Comput Meth Appl Mech Eng, 2019, 345: 1138

[81] Yang Z J, Yabansu Y C, Al—Bahrani R, et al. Deep learning approaches for mining structure—property linkages in high contrast composites from simulation datasets[J]. Comput Mater Sci, 2018, 151: 278

[82] Wang K, Sun W, Du Q. A cooperative game for automated learning of elasto—plasticity knowledge graphs and models with AI—guided experimentation[J]. Comput Mech, 2019, 64（2）: 467

[83] Vlassis N N, Ma R, Sun W. Geometric deep learning for computational mechanics Part I: Anisotropic hyperelasticity[J]. Comput Meth Appl Mech Eng, 2020, 371: 113299

[84] Liu Z L, Bessa M A, Liu W K. Self—consistent clustering analysis: An efficient multi—scale scheme for inelastic heterogeneous materials[J]. Comput Meth Appl Mech Eng, 2016, 306: 319

[85] Liu S M, Lu Y Q, Li J, et al. Multi—scale evolution mechanism and knowledge construction of a digital twin mimic model[J]. Robot Comput Integr Manuf, 2021, 71: 102123

[86] Qu T M, Di S C, Feng Y T, et al. Towards data—driven constitutive modelling for granular materials via micromechanics—informed deep learning[J]. Int J Plast, 2021, 144: 103046

[87] Yan S B, Zou X, Ilkhani M, et al. An efficient multiscale surrogate modelling framework for composite materials considering progressive damage based on artificial neural networks[J]. Compos Part B Eng, 2020, 194: 108014

[88] Wu J J, Yang Y H, Cheng X, et al. The development of digital twin technology review[C]// 2020 Chinese Automation Congress （CAC）, 2020: 4901

[89] Li B, Hou P, Niu L, et al. Survey of cloud—edge collaborative architecture research based on software defined network[J]. Comput Eng Sci, 2021, 43（2）: 242

[90] （李波, 侯鹏, 牛力, 等. 基于软件定义网络的云边协同架构研究综述[J]. 计算机工程与科学, 2021, 43（2）： 242）

[91] Zhao X Y, Cui Y H, Jiang （C/Z） H, et al. CHAIN: Edge computing node placement algorithm based on overlapping domination[J]. J Comput Appl, 2023, 43（9）: 2812

[92] （赵徐炎, 崔允贺, 蒋朝惠, 等. CHAIN：基于重合支配的边缘计算节点放置算法[J]. 计算机应用, 2023, 43（9）： 2812）

[93] Okuda T, Saida T, Matono G, et al. Digital twin framework for real—time dynamic analysis visualization with detecting dynamic changes in structures properties using PINN[C]// Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2023, 2023: 46

[94] He Y, Guo J C, Zheng X L. From surveillance to digital twin: Challenges and recent advances of signal processing for industrial Internet of Things[J]. IEEE Signal Process Mag, 2018, 35（5）: 120

[95] Benitez V H, Pacheco J, Brau A. Thermal field reconstruction on microcontrollers: A physics—informed digital twin using Laplace equation and real—time sensor data[J]. Sensors, 2025, 25（16）: 5130

[96] Liu M N, Fang S L, Dong H Y, et al. Review of digital twin about concepts, technologies, and industrial applications[J]. J Manuf Syst, 2021, 58: 346

[97] Chan C H, Sun M, Huang B. Application of machine learning for advanced material prediction and design[J]. EcoMat. 2022; 4（4）: e12194.

[98] Li F F, Kuang J L, Ji J H, et al. Application of machine learning for predicting the service performance of metallic materials[J]. Chin J Eng, 2024, 46（1）: 120

[99] （李丰范, 匡健隆, 季佳浩, 等. 机器学习在金属材料服役性能预测中的应用[J]. 工程科学学报, 2024, 46（1）： 120）

[100] Jiang M X, Zhang Y J, Yang Z H, et al. A data—driven interpretable method to predict capacities of metal ion doped TiO₂ anode materials for lithium—ion batteries using machine learning classifiers[J]. Inorg Chem Front, 2023, 10（22）: 6646

[101] Shi H C, Li Y J, Zhao S L, et al. Identifying molecular structure—energy level quantitative relationship of thermally activated delayed fluorescence materials using machine learning[J]. J Phys Chem C, 2023, 127（48）: 23526

[102] Li L J, Zhao Y M, Yu H B, et al. An XGBoost algorithm based on molecular structure and molecular specificity parameters for predicting gas adsorption[J]. Langmuir, 2023, 39（19）: 6756

[103] Shen C C, Sheng Q, Zhao H F. Predicting effective thermal conductivity of fibrous and particulate composite materials using convolutional neural network[J]. Mech Mater, 2023, 186: 104804

[104] Xi B, Huang Z W, Al—Obaidi S, et al. Predicting ultra high—performance concrete self—healing performance using hybrid models based on metaheuristic optimization techniques[J]. Constr Build Mater, 2023, 381: 131261

[105] Khan A, Tayara H, Chong K T. Prediction of organic material band gaps using graph attention network[J]. Comput Mater Sci, 2023, 220: 112063

基金资助

国家自然科学基金(52394164)

中央高校基本科研业务费专项资金资助项目(FRF-TP-25-03C)

北京市科技计划课题(Z191100008019002)