{"title":"A physics-informed neural network method for identifying parameters and predicting remaining life of fatigue crack growth","authors":"","doi":"10.1016/j.ijfatigue.2024.108678","DOIUrl":null,"url":null,"abstract":"<div><div>Predicting the remaining life of fatigue cracks is crucial for planning maintenance and repair strategies to prevent untoward incidents. This paper proposes a novel physics-informed neural network (PINN) method for identifying parameters and predicting remaining fatigue crack growth life (FCGL). Initially, the relationship between crack length and fatigue cycles is established through a neural network, and the gradient of fatigue cycles with respect to crack length is obtained by automatic differentiation. Subsequently, a composite loss function is designed to incorporate this gradient within the confines of physical knowledge, ensuring that the established relationship not only aligns with observed data but also adheres to physical knowledge. Furthermore, during the network training, the parameters in physical models are simultaneously updated to better conform to the individuality of the monitored subject. All predicted remaining FCGLs fall within the 1.5 times error band. Compared to purely data-driven or physics-based methods, the proposed method offers more robust and accurate predictions of remaining FCGLs.</div></div>","PeriodicalId":14112,"journal":{"name":"International Journal of Fatigue","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Fatigue","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0142112324005371","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Predicting the remaining life of fatigue cracks is crucial for planning maintenance and repair strategies to prevent untoward incidents. This paper proposes a novel physics-informed neural network (PINN) method for identifying parameters and predicting remaining fatigue crack growth life (FCGL). Initially, the relationship between crack length and fatigue cycles is established through a neural network, and the gradient of fatigue cycles with respect to crack length is obtained by automatic differentiation. Subsequently, a composite loss function is designed to incorporate this gradient within the confines of physical knowledge, ensuring that the established relationship not only aligns with observed data but also adheres to physical knowledge. Furthermore, during the network training, the parameters in physical models are simultaneously updated to better conform to the individuality of the monitored subject. All predicted remaining FCGLs fall within the 1.5 times error band. Compared to purely data-driven or physics-based methods, the proposed method offers more robust and accurate predictions of remaining FCGLs.
期刊介绍:
Typical subjects discussed in International Journal of Fatigue address:
Novel fatigue testing and characterization methods (new kinds of fatigue tests, critical evaluation of existing methods, in situ measurement of fatigue degradation, non-contact field measurements)
Multiaxial fatigue and complex loading effects of materials and structures, exploring state-of-the-art concepts in degradation under cyclic loading
Fatigue in the very high cycle regime, including failure mode transitions from surface to subsurface, effects of surface treatment, processing, and loading conditions
Modeling (including degradation processes and related driving forces, multiscale/multi-resolution methods, computational hierarchical and concurrent methods for coupled component and material responses, novel methods for notch root analysis, fracture mechanics, damage mechanics, crack growth kinetics, life prediction and durability, and prediction of stochastic fatigue behavior reflecting microstructure and service conditions)
Models for early stages of fatigue crack formation and growth that explicitly consider microstructure and relevant materials science aspects
Understanding the influence or manufacturing and processing route on fatigue degradation, and embedding this understanding in more predictive schemes for mitigation and design against fatigue
Prognosis and damage state awareness (including sensors, monitoring, methodology, interactive control, accelerated methods, data interpretation)
Applications of technologies associated with fatigue and their implications for structural integrity and reliability. This includes issues related to design, operation and maintenance, i.e., life cycle engineering
Smart materials and structures that can sense and mitigate fatigue degradation
Fatigue of devices and structures at small scales, including effects of process route and surfaces/interfaces.