Explore the vast range of titles available.

In this paper, guided Lamb wave tomography and eddy current testing (ECT) techniques were combined to locate and evaluate fiber breaks in carbon-fiber-reinforced plastic (CFRP) structures. Guided wave testing (GWT) and computed tomography (CT) imaging were employed to quickly locate fiber breaks in the CFRP plate. From B-scans performed along two different fiber orientations (0 and 90 degrees), parallel-beam projections of different features were extracted from the guided wave signals, using signal-processing techniques (such as wavelet and Hilbert transforms) and statistical functions (such as skewness and kurtosis). The parallel-beam projections of each individual feature were used as input in computed tomography imaging reconstruction to approximately estimate the location of fiber breaks. From the obtained reconstructed images, image-fusion techniques were applied to get complementary information from multiple source images into one single image. After locating the fiber breaks, C-scans were performed in the vicinity of the damage, using an ECT probe with double excitation configuration to evaluate the condition of the fiber break.

Carbon Fiber-Reinforced Polymer (CFRP) composites, widely used across industries, exhibit various damage mechanisms depending on the loading conditions applied. This study employs a structural health monitoring (SHM) approach to investigate the three primary failure modes, fiber breakage, matrix cracking, and delamination, in thermoset quasi-isotropic CFRPs subjected to quasi-static tensile loading until failure. Acoustic emission (AE) signals acquired from an experiment were leveraged to analyze and classify these real-time signals into the failure modes using machine learning (ML) techniques. Due to the extensive number of AE signals recorded during testing, manually classifying these failure mechanisms through waveform inspection was impractical. ML, alongside ensemble learning, algorithms were implemented to streamline the classification, making it more efficient, accurate, and reliable. Conventional AE parameters from the data acquisition system and feature extraction techniques applied to the recorded waveforms were implemented exclusively as classification features to investigate their reliability and accuracy in classifying failure modes in CFRPs. The classification models exhibited up to 99% accuracy, as depicted by evaluation metrics. Further studies, using cross-correlation techniques, ascertained the presence of fiber break events occurring in the bundles as the thermoset CFRP composite approached failure. These findings highlight the significance of integrating machine learning into SHM for the early detection of real-time damage and effective monitoring of residual life in composite materials.

In this study, the variation of fatigue stiffness, fatigue life, and residual strength, as well as the macroscopic damage initiation, expansion, and fracture of CFRP (carbon fiber reinforced polymer) rods in bending-anchored CFRP cable, were investigated experimentally to verify the anchoring performance of the bending anchoring system and evaluate the additional shear effect caused by bending anchoring. Additionally, the acoustic emission technique was used to monitor the progression of critical microscopic damage to CFRP rods in a bending anchoring system, which is closely related to the compression-shear fracture of CFRP rods within the anchor. The experimental results indicate that after the fatigue cycles of two million, the residual strength retention rate of CFRP rod was as high as 95.1% and 76.7% under the stress amplitudes of 500 MPa and 600 MPa, indicating good fatigue resistance. Moreover, the bending-anchored CFRP cable could withstand 2 million cycles of fatigue loading with a maximum stress of 0.4 σult and an amplitude of 500 MPa without obvious fatigue damage. Moreover, under more severe fatigue-loading conditions, it can be found that fiber splitting in CFRP rods in the free section of cable and compression-shear fracture of CFRP rods are the predominant macroscopic damage modes, and the spatial distribution of macroscopic fatigue damage of CFRP rods reveals that the additional shear effect has become the determining factor in the fatigue resistance of the cable. This study demonstrates the good fatigue-bearing capacity of CFRP cable with a bending anchoring system, and the findings can be used for the optimization of the bending anchoring system to further enhance its fatigue resistance, which further promotes the application and development of CFRP cable and bending anchoring system in bridge structures.

Acoustic emission monitoring is a useful technique to deal with detection and identification of damage in composite materials. Over the last few years, identification of damage through intelligent signal processing was particularly emphasized. Data-driven models are developed to predict the remaining useful lifetime. Finite elements modeling (FEM) was used to simulate AE signals due to fiber break and fiber/matrix debonding in a model carbon fiber composite and thereby better understand the AE signals and physical phenomena. This paper presents a computational analysis of AE waveforms resulting from fiber break and fiber/matrix debonding. The objective of this research was to compare the AE signals from a validated fiber break simulation to the AE signals obtained from fiber/matrix debonding and fiber break obtained in several media and to discuss the capability to detect and identify each source.

A sufficient understanding of the failure mechanisms that govern the mechanical behavior and failure modes of natural fiber composites is essential. In this regard, acoustic emission (AE) is a potential technique to monitor the mechanical behaviour and to provide the required information about the failure mechanism of natural fiber-reinforced polymer composites. The purpose and novelty of this study is to investigate for first time, the fracture behaviour of banana/ramie/epoxy composites under a 3-point bending test. During the test procedure, the AE parameters were recorded to evaluate the crack growth from the initial crack to the final fracture of the specimen and to determine the damage locations. AE parameters, such as amplitude, frequency, cumulative hits, and AE energy distributions, were used to identify the failure mechanisms associated with matrix cracking, delamination, fiber-matrix debonding, and fiber breakage. Based on these findings, the cumulative effect of AE events (counts/hits) represents the stress risers that cause failure in the specimen. Because natural fiber composites are brittle materials, they weaken when subjected to tensile loads. For this reason, the outermost bottom layer experienced more failure than the compressive layers during the bending of the specimen. The failure modes were studied using scanning electron microscopy. It was observed from the AE activity that the stress level at the crack initiation is 10–15% higher than the stress magnitude at the fracture stage.

Fibre breaks govern the strength of unidirectional composite materials under tension. The progressive development of fibre breaks is studied using in situ X-ray computed tomography, especially with synchrotron radiation. However, even with synchrotron radiation, the resolution of the time-resolved in situ images is not sufficient for a fully automated analysis of continuous mechanical deformations. We therefore investigate the possibility of increasing the quality of low-resolution in situ scans by means of super-resolution (SR) using 3D deep learning techniques, thus facilitating the subsequent fibre break identification. We trained generative neural networks (GAN) on datasets of high—(0.3 μm) and low-resolution (1.6 μm) statically acquired images. These networks were then applied to a low-resolution (1.1 μm) noisy image of a continuously loaded specimen. The statistical parameters of the fibre breaks used for the comparison are the number of individual breaks and the number of 2-plets and 3-plets per specimen volume. The fully automated process achieves an average accuracy of 82% of manually identified fibre breaks, while the semi-automated one reaches 92%. The developed approach allows the use of faster, low-resolution in situ tomography without losing the quality of the identified physical parameters.

The present paper focuses on experiments and numerical simulation of the acoustic emission (AE) signals due to fiber break in a model composite. AE signals are related to wave effects due to the source, the propagation medium and the sensor. For quantitative AE analysis, it is very important to understand the effect of the piezoelectric sensors and propagation on the “primitive” AE signals. In this study, we investigate the influence of sensors, thickness, and position of the fiber by finite element simulations. This parametric study can allow an enlargement of the library for supervised classification of AE signals.

Understanding the fatigue damage mechanisms in composite materials is of great importance in the wind turbine industry because of the very large number of loading cycles rotor blades undergo during their service life. In this paper, the fatigue damage mechanisms of a non-crimp unidirectional (UD) glass fibre reinforced polymer (GFRP) used in wind turbine blades are characterised by time-lapse ex-situ helical X-ray computed tomography (CT) at different stages through its fatigue life. Our observations validate the hypothesis that off-axis cracking in secondary oriented fibre bundles, the so-called backing bundles, are directly related to fibre fractures in the UD bundles. Using helical X-ray CT we are able to follow the fatigue damage evolution in the composite over a length of 20 mm in the UD fibre direction using a voxel size of (2.75 µm)3. A staining approach was used to enhance the detectability of the narrow off-axis matrix and interface cracks, partly closed fibre fractures and thin longitudinal splits. Instead of being evenly distributed, fibre fractures in the UD bundles nucleate and propagate locally where backing bundles cross-over, or where stitching threads cross-over. In addition, UD fibre fractures can also be initiated by the presence of extensive debonding and longitudinal splitting, which were found to develop from debonding of the stitching threads near surface. The splits lower the lateral constraint of the originally closely packed UD fibres, which could potentially make the composite susceptible to compressive loads as well as the environment in service. The results here indicate that further research into the better design of the positioning of stitching threads, and backing fibre cross-over regions is required, as well as new approaches to control the positions of UD fibres.

Acoustic emission (AE) of fiber break is the sound waves reflecting the stress waves of material during rupturing. It can be employed to evaluate the tensile properties of single fiber during the fiber-bundle tensile test. The key to this technique is to identify AEs of fibers at the break point accurately. However, the severe interference introduced by the background noise and the inter-fiber friction is inevitable. In this paper, a filtering method based on the wavelet de-noising is used to remove the background noise. In order to identify the fiber-broken AEs, a combined parameter that characterizes the amplitude and the density fluctuation along the time axis is proposed. Multiple test results indicate that this parameter can be used to identify wool fiber-broken AEs with accuracy rates higher than 95%.

Acoustic emission (AE) has proven to be a very useful technique for determining damage in ceramic matrix composites (CMCs). CMCs rely on various cracking mechanisms which enable non-linear stress–strain behavior with ultimate failure of the composite due to fiber failure. Since these damage mechanisms are all microfracture mechanisms, they emit stress waves ideal for AE monitoring. These are typically plate waves since, for most specimens or applications, one dimension is significantly smaller than the wavelength of the sound waves emitted. By utilizing the information of the sound waveforms captured on multiple channels from individual events, the location and identity of the sources can often be elucidated. The keys to the technique are the use of wide-band frequency sensors, digitization of the waveforms (modal AE), strategic placement of sensors to sort the data and acquire important contents of the waveforms pertinent for identification, and familiarity with the material as to the damage mechanisms occurring at prescribed points of the stress history. The AE information informs the damage progression in a unique way, which adds to the understanding of the process of failure for these composites. The AE methodology was applied to woven SiC fiber-reinforced melt-infiltrated SiC matrix composites tested in fatigue (R = 0.1) at different frequencies. Identification of when and where AE occurred coupled with waveform analysis led to source identification and failure progression. For low frequency fatigue conditions, damage progression leading to failure appeared to be due to fiber failure at or near the peak stress of the stress cycle. For higher frequency fatigue conditions, significantly greater amounts of AE were detected compared to low frequency tests a few hours prior to failure. Damage progression leading to failure included AE detected events which occurred on the unloading part of the fatigue cycle near the valley of the stress cycle. These events were associated with 90 tow longitudinal split and shear cracks presumably due to local compressive stresses associated with mating crack surface interactions during unloading. The local region where these occurred was the eventual failure location and the “valley” events appeared to influence the formation of increased local transverse cracking based on AE.