Fatigue damage modeling of composite structures: the Onera viewpoint

Kaminski, Myriam; Laurin, Frédéric; Maire, Jean-François; Rakotoarisoa, C.; Hemon, E.

doi:10.12762/2015.al09-06

Cited by 12 publications

(9 citation statements)

References 68 publications

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“…Slight variations in the design of novel composite materials or their operating and storage conditions result in extensive growth of experimental testing campaigns required to characterise fatigue durability and assess their lifetime [ 128 ]. Fatigue models reduce the number of tests necessary for long-term predictions of the behaviour of composites under cyclic loading.…”

Section: Models For Predicting Materials Durability and Service Lifetimementioning

confidence: 99%

Modelling of Environmental Ageing of Polymers and Polymer Composites—Durability Prediction Methods

et al. 2022

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Polymers and polymer composites are negatively impacted by environmental ageing, reducing their service lifetimes. The uncertainty of the material interaction with the environment compromises their superior strength and stiffness. Validation of new composite materials and structures often involves lengthy and expensive testing programs. Therefore, modelling is an affordable alternative that can partly replace extensive testing and thus reduce validation costs. Durability prediction models are often subject to conflicting requirements of versatility and minimum experimental efforts required for their validation. Based on physical observations of composite macroproperties, engineering and phenomenological models provide manageable representations of complex mechanistic models. This review offers a systematised overview of the state-of-the-art models and accelerated testing methodologies for predicting the long-term mechanical performance of polymers and polymer composites. Accelerated testing methods for predicting static, creep, and fatig ue lifetime of various polymers and polymer composites under environmental factors’ single or coupled influence are overviewed. Service lifetimes are predicted by means of degradation rate models, superposition principles, and parametrisation techniques. This review is a continuation of the authors’ work on modelling environmental ageing of polymer composites: the first part of the review covered multiscale and modular modelling methods of environmental degradation. The present work is focused on modelling engineering mechanical properties.

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Section: Models For Predicting Materials Durability and Service Lifetimementioning

confidence: 99%

Modelling of Environmental Ageing of Polymers and Polymer Composites—Durability Prediction Methods

et al. 2022

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“…Hereafter, considering the difference of damage evolution with the number of cycles among composites and metals (see Fig. 8) [85], Shokrieh and Rafiee [62] developed a new flowchart of accumulated fatigue damage modelling, in which a generalized material property degradation-based damage rule is utilized. In particular, to accurately describe the nonlinear fatigue damage development of composites in the three periods (matrix crack, delamination, interfacial debonding and fiber breakage), Liu et al [86] presented an improved composite fatigue damage model considering stiffness evolution.…”

Section: Fatigue Reliability On Bladesmentioning

confidence: 99%

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

et al. 2022

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“…41,42 Damage predominantly arises during two points in the lifespan of a composite: manufacturing and service life. [43][44][45][46] Table 1 contains examples of mechanisms occurring during these two distinct points in the lifetime of a composite. Heslehurst 47 categorized 52 damage mechanisms present in composite materials based on the time they appear in the lifespan of a composite, their size, and their location.…”

Section: Structural Health Monitoringmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

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Fatigue damage modeling of composite structures: the Onera viewpoint

Cited by 12 publications

References 68 publications

Modelling of Environmental Ageing of Polymers and Polymer Composites—Durability Prediction Methods

Modelling of Environmental Ageing of Polymers and Polymer Composites—Durability Prediction Methods

Fatigue reliability of wind turbines: historical perspectives, recent developments and future prospects

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

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