Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique

Barile, Claudia; Casavola, Caterina; Pappalettera, Giovanni; Kannan, Vimalathithan Paramsamy

doi:10.1016/j.apacoust.2021.108425

Cited by 42 publications

(19 citation statements)

References 27 publications

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“…And the ones of cluster‐2 in these two composites increase from 25.5 and 26.3 µs to 33.7 and 33.1 µs, respectively. The definition of risetime is the duration at which the AE signal first crosses its threshold 26 . The change of mean value risetime illustrates that oxidation can change the waveform of AE signal of damage mode in composites.…”

Section: Resultsmentioning

confidence: 99%

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The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Shan

Xue

2022

J Am Ceram Soc.

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To study the anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere, the damage evolution of composites after oxidation was explored by unsupervised machine learning technology (k‐means). Results display that the mean feature values of cluster‐1 (some small cracks in oxidation layer and matrix as well as fiber debonding) in composites modified with Al2O3 are larger than that in virgin after oxidation. Meanwhile, as the oxidation time increases, the concentrated area of cluster‐2 (fiber breakage, cracks in axial and transverse yarns) in both composites gradually shifts toward the direction of high mean feature values. Because Al2O3 can protect the BN from oxidation, the increase of peak frequency and risetime of composites modified with Al2O3 is less than that of virgin. Moreover, the proportion of cluster‐3 (big transverse matrix cracks) in composites modified with Al2O3 is always more than that of virgin. Based on the research results, the corresponding relationship between each damage behavior and each cluster is thoroughly built.

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Section: Resultsmentioning

confidence: 99%

“…The definition of risetime is the duration at which the AE signal first crosses its threshold. 26 The change of mean value risetime illustrates that oxidation can change the waveform of AE signal of damage mode in composites. 1).…”

Section: Specimens Oxidation For 100 Hmentioning

confidence: 99%

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Shan

Xue

2022

J Am Ceram Soc.

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“…Thus, in this study, the centroid frequency was calculated from the frequency spectrum to investigate the change in the frequency of AE signals during FCG. The definitions of these parameters can be found in several research journal papers and books [ 1 , 9 , 20 , 26 ]. The explanations of some important AE parameters used in this study are presented in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

Characterization of Fatigue Crack Growth Based on Acoustic Emission Multi-Parameter Analysis

et al. 2022

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In engineering structures that are subject to cyclic loading, monitoring and assessing fatigue crack growth (FCG) plays a crucial role in ensuring reliability. In this study, the acoustic emission (AE) technique was used to monitor the FCG behavior of 2.25Cr1Mo0.25V steel in real-time. Specifically, an AE multi-parameter analysis was conducted to qualitatively assess the crack growth condition and quantitatively correlate the crack growth rate with AE. Various AE parameters were extracted from AE signals, and the performances of different AE parameters were analyzed and discussed. The results demonstrated that four stages of FCG, which correspond to macrocrack initiation, stable crack growth with low crack growth rate, stable crack growth with high crack growth rate, and unstable crack growth, are distinctly identified by several AE time domain parameters. The sudden and continuous occurrence of many AE signals with high count (>100) and high energy (>40 mV·ms) can provide early and effective warning signs for accelerated crack growth before final failure occurs. Moreover, linear correlations between crack growth rate and different AE parameters are established for quantifying crack growth. Based on the AE multi-parameter analysis, it was found that the count, energy, and kurtosis are superior AE parameters for both qualitatively and quantitatively characterizing the FCG in 2.25Cr1Mo0.25V steel. Results from this research provide an AE strategy based on multi-parameter analysis for effective monitoring and assessment of FCG in engineering materials.

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“…Counts are the number of instances the AE signal peaks cross the detection threshold. This parameter has been successfully used by several researchers in identifying the damage initiation or predicting the failure in composites [34][35][36][37][38]. In this work, the preliminary analysis of the AE signals is carried out by comparing the counts with the load-displacement data of the SBS test.…”

Section: Acoustic Emission Analysismentioning

confidence: 99%

Investigation of Interlaminar Shear Properties of CFRP Composites at Elevated Temperatures Using the Lempel-Ziv Complexity of Acoustic Emission Signals

et al. 2022

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Three-point bending tests on Short Beam Shear (SBS) specimens are performed to investigate the interlaminar shear properties of plain weave fabric CFRP composites. The tests are performed in a controlled environmental chamber at two different elevated temperatures. The interlaminar shear properties of the specimens remain largely unaffected by the testing temperature. However, the SEM micrographs show different damage progressions between the specimens tested at 100 °C and 120 °C. Fibre ruptures and longer delamination between the plies, as a result of a high temperature, are observed in the specimens tested at 120 °C, which are not observed in the specimens tested at 100 °C. In addition, the acoustic emission activities during the tests are investigated by using piezoelectric sensors. The information-theoretic parameter, the Lempel-Ziv (LZ) complexity, is calculated for the recorded acoustic signals. The LZ Complexities are used for identifying the occurrence of the first delamination failure in the specimens. Additionally, the two features of the acoustic signals, LZ complexity and Weighted Peak Frequency (W.P-Freq), are used for distinguishing the different damage sources in the CFRP specimens. The results are well-supported by the time-frequency analysis of the acoustic signals using a Continuous Wavelet Transform (CWT).

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Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique

Cited by 42 publications

References 27 publications

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

Characterization of Fatigue Crack Growth Based on Acoustic Emission Multi-Parameter Analysis

Investigation of Interlaminar Shear Properties of CFRP Composites at Elevated Temperatures Using the Lempel-Ziv Complexity of Acoustic Emission Signals

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Laplacian score and K-means data clustering for damage characterization of adhesively bonded CFRP composites by means of acoustic emission technique

Cited by 42 publications

References 27 publications

The anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiCf/SiC–B4C modified with Al2O3 in wet atmosphere based on machine learning

Characterization of Fatigue Crack Growth Based on Acoustic Emission Multi-Parameter Analysis

Investigation of Interlaminar Shear Properties of CFRP Composites at Elevated Temperatures Using the Lempel-Ziv Complexity of Acoustic Emission Signals

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The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning

The anti‐oxidation mechanism of SiC_f/SiC–B₄C modified with Al₂O₃ in wet atmosphere based on machine learning