Correlation of Acoustic Emission with Fractography in Bending of Glass–Epoxy Composites

Panek, Maciej; Błażewicz, S.; Konsztowicz, Krzysztof J.

doi:10.1007/s10921-020-00706-0

Cited by 8 publications

(5 citation statements)

References 22 publications

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“…Nonetheless, the predominant matrix cracking was found along the transverse and longitudinal directions in 32 layers specimen which is depicted in Figure 7C. The mechanical and AE results reveal that the multiple load drops and % of AE hits related to matrix cracking failure modes were found to be very high which confirmed the dominance of matrix cracking in 32 layers specimen 40 …”

Section: Resultsmentioning

confidence: 72%

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The influence of thickness and recycled milled glass fiber fillers on the delamination resistance of polymer composite angle brackets

Venkatesan,

Vellayaraj,

Chelliah

2023

Polymer Composites

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Composite structures with L, C, and T shape geometries are commonly used in the design of aerospace structural components. These complex structures are prone to delamination failure at the curved region. This study investigates the effect of thickness and recycled milled glass fiber fillers to improve the strength properties and delamination resistance of glass/epoxy composite angle brackets. Different thicknesses of 3.5 mm (16 layers), 5 mm (24 layers), and 6.5 mm (32 layers) specimens were subjected to four‐point bending test with acoustic emission (AE) monitoring for evaluating damage resistance. Mechanical results reveal that the specimen with a higher thickness of 6.5 mm has better damage tolerance, followed by 3.5 and 5 mm angle brackets. Moreover, the location of AE signals and % of AE hits noticed the initiation and propagation of delamination in 5 mm thickness specimen at the earlier stage of loading. However, the curved beam strength and interlaminar tensile strength (ILTS) of 5 mm thickness specimen were improved by 31.02% and 19.2% respectively, through the incorporation of 2 wt% of fillers. Further, the interfacial adhesion between the fillers and epoxy matrix was studied using Fourier‐transform infrared analysis to confirm the improvement of delamination resistance in 5 mm thickness specimen. Finally, the mechanical and AE results were well correlated with field emission scanning electron microscope images to characterize the damage mechanisms in composite angle brackets.Highlights Effect of thickness on the delamination resistance of composites was studied. Interlaminar properties of composite angle brackets were assessed. Acoustic emission parameters were used to study the damage mechanisms. Recycled milled glass fillers were employed to improve delamination resistance. Mechanical and acoustic emission results were correlated with field emission scanning electron microscope images.

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Section: Resultsmentioning

confidence: 72%

“…The mechanical and AE results reveal that the multiple load drops and % of AE hits related to matrix cracking failure modes were found to be very high which confirmed the dominance of matrix cracking in 32 layers specimen. 40…”

Section: Fractographic Analysismentioning

confidence: 99%

The influence of thickness and recycled milled glass fiber fillers on the delamination resistance of polymer composite angle brackets

Venkatesan,

Vellayaraj,

Chelliah

2023

Polymer Composites

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“…During composite loading, several damage mechanisms occur almost simultaneously, which creates a scientific challenge to assign a specific set of AE signal features to a particular damage mechanism. This is now most frequently solved with pattern recognition [5,6]. Tang et al [7] used a sequential feature selection method based on a k-means clustering algorithm for the classification of AE signals in wind turbine blades loaded in the flap-wise direction.…”

Section: Introductionmentioning

confidence: 99%

Characterization of optical ae signals at loading of fibre reinforced composites

Misson¹,

Potočnik²,

Bergant³

et al. 2023

eJNDT

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Characterization of acoustic emission (AE) signals in loaded materials can reveal structural damage and consequently provide early warnings about product failures. Therefore, extraction of the most informative features from AE signals is an important part of the characterization process. This study considers the characterization of AE signals obtained from bending experiments for biocomposite and glass fiber epoxy (GFE) composites at room temperature and low temperature. For the acquisition of AE signals, fiber optic sensors (FOS) are used that can outperform classical electrical sensors under challenging operational environments. In this paper, we propose the extraction of deep features using different machine learning methods. The deep features are compared with extracted standard AE features. Then, the different feature sets are analyzed through discriminant analysis, neural networks, and extreme learning machines, combined with feature selection, to estimate the predictive potential of various feature sets. The proposed signal processing structure is focused on the classification of AE signals to recognize the source material, evaluate the predictive importance of extracted features, and evaluate the ability of used FOS for evaluation of material behavior under challenging low-temperature environments.

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“…This presents a scientific challenge to assign a specific set of AE signal features to a particular damage mechanism. Often, various machine learning techniques [17,[31][32][33] are used in combination with feature selection [34] and Hilbert-Huang transform to extract frequency descriptors [35]. For the automatic extraction of the intrinsic characteristics of signals, deep learning methods are becoming increasingly popular.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Biocomposites and Glass Fiber Epoxy Composites Based on Acoustic Emission Signals, Deep Feature Extraction, and Machine Learning

Kek

Potočnik

Misson³

et al. 2022

Sensors

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This study presents the results of acoustic emission (AE) measurements and characterization in the loading of biocomposites at room and low temperatures that can be observed in the aviation industry. The fiber optic sensors (FOS) that can outperform electrical sensors in challenging operational environments were used. Standard features were extracted from AE measurements, and a convolutional autoencoder (CAE) was applied to extract deep features from AE signals. Different machine learning methods including discriminant analysis (DA), neural networks (NN), and extreme learning machines (ELM) were used for the construction of classifiers. The analysis is focused on the classification of extracted AE features to classify the source material, to evaluate the predictive importance of extracted features, and to evaluate the ability of used FOS for the evaluation of material behavior under challenging low-temperature environments. The results show the robustness of different CAE configurations for deep feature extraction. The combination of classic and deep features always significantly improves classification accuracy. The best classification accuracy (80.9%) was achieved with a neural network model and generally, more complex nonlinear models (NN, ELM) outperform simple models (DA). In all the considered models, the selected combined features always contain both classic and deep features.

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Correlation of Acoustic Emission with Fractography in Bending of Glass–Epoxy Composites

Cited by 8 publications

References 22 publications

The influence of thickness and recycled milled glass fiber fillers on the delamination resistance of polymer composite angle brackets

The influence of thickness and recycled milled glass fiber fillers on the delamination resistance of polymer composite angle brackets

Characterization of optical ae signals at loading of fibre reinforced composites

Characterization of Biocomposites and Glass Fiber Epoxy Composites Based on Acoustic Emission Signals, Deep Feature Extraction, and Machine Learning

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