Damage evolution behavior of bi-adhesive repaired composites under bending load by acoustic emission and micro-CT

Ji, Xiao-long; Zhou, Wei; Sun, Heng; Liu, Jia; Ma, Lianhua

doi:10.1016/j.compstruct.2021.114742

Cited by 31 publications

(13 citation statements)

References 46 publications

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“…However, as a typical signals belonging to different damage types might have similar amplitude and energy levels and therefore overlapping regions, making it challenging to categorize the signals. To address this problem, previous studies [17][18][19] revealed that the peak frequency data is a good choice for clustering and results in more accurate classification of damage mechanisms. Additionally, the decision of which cluster corresponds to which damage mechanism is greatly influenced by the cumulative energy data.…”

Section: Resultsmentioning

confidence: 99%

“…However, it was seen from these studies that solely the amplitude data was not sufficient to distinguish damage mechanisms rationally. Therefore, to alleviate this ambiguity, several authors suggested the use of peak frequency and cumulative energy data for well-distinguished damage detection [17][18][19]. For instance, Pashmforoush et al [19] stated that the k-means genetic algorithm is one of the most suitable clustering algorithms for damage classification using the peak frequency and the cumulative energy data.…”

Section: Introductionmentioning

confidence: 99%

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Damage Detection of CNT/CNC-reinforced Foam-cored Sandwich Composites by Acoustic Emission Tests under Flexural Load

Kucukkalfa

Ghaderiaram

Yıldız

et al. 2023

AIAA SCITECH 2023 Forum

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Sandwich composites stand out especially in the aerospace industry owing to their high strength-to-weight ratio, one of the most prominent factors for material selection. Polymeric foams as core material in sandwich composites are likely to prevent delamination between face sheets and core by augmenting the contact surface area, resulting from their closed-cell structure. Polymeric foam properties can be enhanced by adding nanomaterials such as carbon nanotubes (CNTs), however increased CNT content or the type of CNTs might arise critical problems such as agglomeration and irregular distribution of nanomaterials. Cellulose nanocrystals (CNCs) are claimed to be good candidates to prevent nanomaterial reinforcing related issues and further, their inclusion enables reinforcing of polymeric foams using an optimum CNT/CNC concentration. In this work, CNT/CNC reinforced polyurethane (PU) foam-cored sandwich composites were manufactured and characterized for the influence of nanomaterial addition on the mechanical properties with an aim to find the optimum nanomaterial content. 0.1 wt.% CNT, CNC, CNT/CNC (1:1), and CNT/CNC (1:2) reinforced PU foam-cored sandwich composites were subjected to simultaneous three-point bending tests and acoustic emission tests, one of the promising non-destructive testing methods enabling insitu monitoring of the damage mechanisms to understand how damage evolves. The effects of these nanomaterial additives on damage mechanisms and the mechanical properties were

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Damage Detection of CNT/CNC-reinforced Foam-cored Sandwich Composites by Acoustic Emission Tests under Flexural Load

Kucukkalfa

Ghaderiaram

Yıldız

et al. 2023

AIAA SCITECH 2023 Forum

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“…The features of AE event, including amplitude, duration, rise time, counts, energy, centroid frequency, peak frequency, and RA value, recorded during the three-point bending test were selected for correlation analysis and principal component analysis (PCA). In our previous study, 17,35 peak frequency, amplitude, and RA value were selected for unsupervised clustering analysis. Then, the number of cluster k was calculated using the range from 2 to 10 and two cluster validity techniques, namely, DB index and Sil index were used to evaluate the best clustering performance.…”

Section: Resultsmentioning

confidence: 99%

Cluster analysis on damage pattern recognition in carbon/epoxy composites using acoustic emission wavelet packet

Qiao

Zhou

Liang

et al. 2022

Journal of Reinforced Plastics and Composites

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The purpose of this paper is to investigate the damage pattern recognition in carbon/epoxy composite laminates by Acoustic Emission technique. Three specimens with layup of [90/0/90/0]s were fabricated and subjected to three-point bending test. The dataset was clustered and analyzed by three unsupervised algorithms, namely, k-means, Self-Organizing Map, and Fuzzy C-Means. The results show that the signals can be divided into four clusters, which correspond to matrix cracking, delamination, fiber/matrix debonding, and fiber breakage, respectively. A method of energy feature extraction based on wavelet packet decomposition was used to analyze the clustering center to obtain the frequency band with relatively high energy corresponding each damage mode further determined the actual damage pattern of these signals with disputed classification results. The results of k-means illustrated that little difference in the determination of matrix cracking, preferring to assign more signal to inter-laminar damage and higher peak frequency to fiber breakage compared to the other two algorithms. It can verify the clustering results well and k-means algorithm performs better in damage pattern recognition of composites.

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“…To ignore background noise, a threshold of 10 mV was established. Based on our previous research, [24] the values of peak definition time (PDT), hit definition time (HDT), and hit lock out time (HLT) parameters were set to 30 μs, 150 μs, and 300 μs in the tests respectively.…”

Section: Experimental Equipmentmentioning

confidence: 99%

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

et al. 2023

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Combining acoustic emission (AE) and machine learning algorithms to understand damage and failure of carbon fiber reinforced polymer (CFRP) under bending loads is a challenging task in their practical applications. This work aims to identify different characteristics of the signal induced by damage patterns of carbon/epoxy composites using machine learning model. By carrying out wavelet packet analysis of the AE signals allowing the identification of four macroscopic damage modes. Four types of signals corresponding to matrix cracking, delamination, fiber/matrix debonding and fiber breakage are characterized by the frequency bands of the main energy distribution in the original waveform. The frequency bands are (62.5–125 kHz) or (125–187.5 kHz), (187.5–250 kHz), (250–312.5 kHz) and (312.5–375 kHz) and above, respectively. To test the actual performance of the established model, called support vector machine (SVM) classifier, several precracked and untreated specimens have been fabricated and subjected to three‐point bending test. The classification result of constructed classifier was compared with the k‐means algorithm, which is widely accepted for classifying AE signals, and the similarity of the two results is analyzed. The results indicated that the similarity of different clusters exceeded 92%, 84%, 83% and 77%, respectively. It can be seen that the SVM classifier was considered promising to provide new ideas for the health monitoring of composite structures.

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Damage evolution behavior of bi-adhesive repaired composites under bending load by acoustic emission and micro-CT

Cited by 31 publications

References 46 publications

Damage Detection of CNT/CNC-reinforced Foam-cored Sandwich Composites by Acoustic Emission Tests under Flexural Load

Damage Detection of CNT/CNC-reinforced Foam-cored Sandwich Composites by Acoustic Emission Tests under Flexural Load

Cluster analysis on damage pattern recognition in carbon/epoxy composites using acoustic emission wavelet packet

Damage mode identification in carbon/epoxy composite via machine learning and acoustic emission

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