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Damage Analysis and Reliability Assessment for Composite Materials

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A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Advanced Composites".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 6898

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Prof. Dr. Joachim Hausmann

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Department Component Development, Leibniz-Insitut für Verbundwerkstoffe GmbH, 67663 Kaiserslautern, Germany
Interests: composite materials; hybrid materials; component design; mechanical testing; stress analyses; residual stresses
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Dear Colleagues,

Composite materials such as continuous fiber-reinforced polymers are marked by anisotropic behavior and particular failure mechanisms. Engineering methods established for metals such as equivalent stress concepts for stress analysis are not suitable for composite materials. Therefore, the analysis of damage and failure behavior needs special consideration. Numerous specific testing procedures and analysis methods have been developed to investigate material behavior under mechanical loading. Failure concepts have been proposed to describe fracture behavior under the consideration of multi-axial stress states. In summary, research results have to be processed to enable the design of reliable high-performance composite structures.

Despite about half a century of research on the mechanical behavior and failure mechanisms of fiber-reinforced composite materials, many questions are still open. This Special Issue on “Damage Analysis and Reliability Assessment for Composite Materials” is related to recent research results on the analysis of damage mechanisms, failure concepts and influencing factors such as constituent properties or environmental conditions. These are the basis to assess the reliability of composite materials in structural applications. Static, cyclic, dynamic and transient loadings are considered. The focus is on continuous fiber-reinforced polymers (FRP) for structural applications, but contributions on other types of reinforcement or matrices are also welcome.

Prof. Dr. Joachim Hausmann
Guest Editor

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Keywords

composite materials
fracture
damage
failure behavior
mechanical testing
failure concepts
characterization

Published Papers (4 papers)

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Research

18 pages, 7614 KiB

Open AccessEditor’s ChoiceArticle

First Conclusions on Damage Behaviour of Recycled Carbon Staple Fibre Yarn Using X-ray and Acoustic Emission Techniques

by Christian Becker, Joachim Hausmann, Janna Krummenacker and Nicole Motsch-Eichmann

Materials 2023, 16(13), 4842; https://doi.org/10.3390/ma16134842 - 5 Jul 2023

Cited by 1 | Viewed by 1121

Abstract

This paper presents the first results on the characterisation of the damage behaviour of recycled carbon fibre (rCF) rovings manufactured into unidirectionally (UD) reinforced plates. In the first step, the mechanical properties of several material combinations were determined by mechanical tests (tensile, flexural, compression). This proves the usability of the material for load-bearing structures. For example, a tensile modulus of up to 80 GPa and a tensile strength of 800 MPa were measured. Subsequently, the fracture surface was analysed by scanning electron microscopy (SEM) to characterise the fibre–matrix adhesion and to obtain first indications of possible failure mechanisms. Despite the high mechanical properties, poor fibre–matrix adhesion was found for all matrix systems. In situ X-ray microscopy tests were then performed on smaller specimens under predefined load levels as transverse tensile and bending tests. The results provide further predictions of the failure behaviour and can be compared to the previous test results. The three-dimensional scan reconstruction results were used to visualise the failure behaviour of the staple fibres in order to detect fibre pull-out and fibre or inter-fibre failure and to draw initial conclusions about the damage behaviour in comparison to conventional fibre composites. In particular, a benign failure behaviour in the transverse tensile test was demonstrated with this procedure. In addition, first concepts and tests for the integration of AE analysis into the in situ setup of the X-ray microscope are presented. Full article

(This article belongs to the Special Issue Damage Analysis and Reliability Assessment for Composite Materials)

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12 pages, 19869 KiB

Open AccessArticle

An Image-Based Framework for Measuring the Prestress Level in CFRP Laminates: Experimental Validation

by Jónatas Valença, Cláudia Ferreira, André G. Araújo and Eduardo Júlio

Materials 2023, 16(5), 1813; https://doi.org/10.3390/ma16051813 - 22 Feb 2023

Viewed by 1127

Abstract

Image-based methods have been applied to support structural monitoring, product and material testing, and quality control. Lately, deep learning for compute vision is the trend, requiring large and labelled datasets for training and validation, which is often difficult to obtain. The use of synthetic datasets is often applying for data augmentation in different fields. An architecture based on computer vision was proposed to measure strain during prestressing in CFRP laminates. The contact-free architecture was fed by synthetic image datasets and benchmarked for machine learning and deep learning algorithms. The use of these data for monitoring real applications will contribute towards spreading the new monitoring approach, increasing the quality control of the material and application procedure, as well as structural safety. In this paper, the best architecture was validated during experimental tests, to evaluate the performance in real applications from pre-trained synthetic data. The results demonstrate that the architecture implemented enables estimating intermediate strain values, i.e., within the range of training dataset values, but it does not allow for estimating strain values outside those range. The architecture allowed for estimating the strain in real images with an error ∼0.5%, higher than that obtained with synthetic images. Finally, it was not possible to estimate the strain in real cases from the training performed with the synthetic dataset. Full article

(This article belongs to the Special Issue Damage Analysis and Reliability Assessment for Composite Materials)

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24 pages, 2755 KiB

Open AccessEditor’s ChoiceArticle

In-Service Delaminations in FRP Structures under Operational Loading Conditions: Are Current Fracture Testing and Analysis on Coupons Sufficient for Capturing the Essential Effects for Reliable Predictions?

by Andreas J. Brunner, René Alderliesten and John-Alan Pascoe

Materials 2023, 16(1), 248; https://doi.org/10.3390/ma16010248 - 27 Dec 2022

Cited by 3 | Viewed by 1642

Abstract

Quasi-static or cyclic loading of an artificial starter crack in unidirectionally fibre-reinforced composite test coupons yields fracture mechanics data—the toughness or strain-energy release rate (labelled G)—for characterising delamination initiation and propagation. Thus far, the reproducibility of these tests is typically between 10 and 20%. However, differences in the size and possibly the shape, but also in the fibre lay-up, between test coupons and components or structures raise additional questions: Is G from a coupon test a suitable parameter for describing the behaviour of delaminations in composite structures? Can planar, two-dimensional, delamination propagation in composite plates or shells be properly predicted from essentially one-dimensional propagation in coupons? How does fibre bridging in unidirectionally reinforced test coupons relate to delamination propagation in multidirectional lay-ups of components and structures? How can multiple, localised delaminations—often created by impact in composite structures—and their interaction under service loads with constant or variable amplitudes be accounted for? Does planar delamination propagation depend on laminate thickness, thickness variation or the overall shape of the structure? How does exposure to different, variable service environments affect delamination initiation and propagation? Is the microscopic and mesoscopic morphology of FRP composite structures sufficiently understood for accurate predictive modelling and simulation of delamination behaviour? This contribution will examine selected issues and discuss the consequences for test development and analysis. The discussion indicates that current coupon testing and analysis are unlikely to provide the data for reliable long-term predictions of delamination behaviour in FRP composite structures. The attempts to make the building block design methodology for composite structures more efficient via combinations of experiments and related modelling look promising, but models require input data with low scatter and, even more importantly, insight into the physics of the microscopic damage processes yielding delamination initiation and propagation. Full article

(This article belongs to the Special Issue Damage Analysis and Reliability Assessment for Composite Materials)

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18 pages, 3496 KiB

Open AccessEditor’s ChoiceArticle

Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning

by Jónatas Valença, Habibu Mukhandi, André G. Araújo, Micael S. Couceiro and Eduardo Júlio

Materials 2022, 15(18), 6310; https://doi.org/10.3390/ma15186310 - 11 Sep 2022

Cited by 14 | Viewed by 2317

Abstract

The strengthening of concrete structures with laminates of Carbon-Fiber-Reinforced Polymers (CFRP) is a widely adopted technique. retained The application is more effective if pre-stressed CFRP laminates are adopted. The measurement of the strain level during the pre-stress application usually involves laborious and time-consuming applications of instrumentation. Thus, the development of expedited approaches to accurately measure the pre-stressed application in the laminates represents an important contribution to the field. This paper proposes and benchmarks contact-free architecture for measuring the strain level of CFRP laminate based on computer vision. The main objective is to provide a solution that might be economically feasible, automated, easy to use, and accurate. The architecture is fed by digitally deformed synthetic images, generated based on a low-resolution camera. The adopted methods range from traditional machine learning to deep learning. Furthermore, dropout and cross-validation methods for quantifying traditional machine learning algorithms and neural networks are used to efficiently provide uncertainty estimates. ResNet34 deep learning architecture provided the most accurate results, reaching a root mean square error (RMSE) of 0.057‰ for strain prediction. Finally, it is important to highlight that the architecture presented is contact-free, automatic, cost-effective, and measures directly on the laminate surfaces, which allows them to be widely used in the application of pre-stressed laminates. Full article

(This article belongs to the Special Issue Damage Analysis and Reliability Assessment for Composite Materials)

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