Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel

Sikdar, Shirsendu; Liu, Dianzi; Kundu, Amit

doi:10.1016/j.compositesb.2021.109450

Cited by 84 publications

(30 citation statements)

References 34 publications

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“…As seen in Section 1, two-dimensional CNNs have become popular for acoustic emission signal processing in recent years. The literature [45] has noted that the AE signals (time domain) in composite materials can be converted to time-frequency scalograms by performing continuous wavelet transform and that these scalogram images can be used as inputs to the CNN architecture while achieving high accuracies of 94.3-97.1%. Therefore, an interesting comparison was performed on the obtained datasets.…”

Section: Experimental Data and Ae Datasetsmentioning

confidence: 99%

“…The broadband acoustic emission sensors enabled the full frequency spectrum of the signal to be recorded and the waveform to be digitally stored. It is not standard practice to perform a frequency spectrum analysis of the acoustic emission signals collected by broadband AE sensors [44,45].…”

Section: Frequency-domain Sequence Datamentioning

confidence: 99%

“…In Section 2.3, the obtained AE datasets were used with the method and CNN architecture proposed in [45], and the damage classification test accuracy was around 84.6%, while in Section 3.2, the test accuracy of the method proposed in this paper was around 99%. This indicates that the deep learning method proposed in this paper greatly improves the damage classification accuracy for AE data in composite materials.…”

Section: Comparative Analysismentioning

confidence: 99%

“…Therefore, AE technology and deep learning are linked and have been adopted by many researchers [7,[35][36][37][38][39][40][41]. The conversion of time series data into two-dimensional image data using short fast Fourier transform, wavelet transform, and the classification of acoustic emission data [42][43][44][45][46] using two-dimensional convolutional neural networks (CNNs) is a common method that has been used by many researchers.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Fu-ping

Jiang

et al. 2022

Materials

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Damage detection and the classification of carbon fiber-reinforced composites using non-destructive testing (NDT) techniques are of great importance. This paper applies an acoustic emission (AE) technique to obtain AE data from three tensile damage tests determining fiber breakage, matrix cracking, and delamination. This article proposes a deep learning approach that combines a state-of-the-art deep learning technique for time series classification: the InceptionTime model with acoustic emission data for damage classification in composite materials. Raw AE time series and frequency-domain sequence data are used as the input for the InceptionTime network, and both obtain very high classification performances, achieving high accuracy scores of about 99%. The InceptionTime network produces better training, validation, and test accuracy with the raw AE time series data than it does with the frequency-domain sequence data. Simultaneously, the InceptionTime model network shows its potential in dealing with data imbalances.

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Section: Experimental Data and Ae Datasetsmentioning

confidence: 99%

Section: Frequency-domain Sequence Datamentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Fu-ping

Jiang

et al. 2022

Materials

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“…In the recent years, Deep Learning has been used contemporarily with the AE technique for damage characterization [18]. Convolutional Neural Networks (CNN) have been used for identifying damage modes in SiC composites, CORTEN steel and civil structures [19][20][21][22][23][24][25], while Artificial Neural Networks (ANN) have been used for corrosion monitoring of steel. While both these networks can relate a large number of parameters and building a model for classification and prediction in real-time, CNN is preferred generally for image-based analysis.…”

Section: Introductionmentioning

confidence: 99%

Damage Progress Classification in AlSi10Mg SLM Specimens by Convolutional Neural Network and k-Fold Cross Validation

et al. 2022

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In this study, the damage evolution stages in testing AlSi10Mg specimens manufactured using Selective Laser Melting (SLM) process are identified using Acoustic Emission (AE) technique and Convolutional Neural Network (CNN). AE signals generated during the testing of AlSi10Mg specimens are recorded and analysed to identify their time-frequency features in three different damage evolution stages: elastic stage, plastic stage, and fracture stage. Continuous Wavelet Transform (CWT) spectrograms are used for the processing of the AE signals. The AE signals from each of these stages are then used for training a CNN based on SqueezeNet. Moreover, k-fold cross validation is implemented while training the modified SqueezeNet to improve the classification efficiency of the network. The trained network shows promising results in classifying the AE signals from different damage evolution stages.

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Effects of thermal aging on damage evolution behavior of glass fiber reinforced composites with multilayer graphene by acoustic emission

Wang

Zhou

Guo

et al. 2022

J of Applied Polymer Sci

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The thermal aging behavior of glass fiber reinforced composites has attracted much attention because of its application in elevated temperatures environments. In this study, to improve the thermal aging resistance performance of composites, multilayer graphene was embedded in glass fiber reinforced composites and the damage evolution behaviors of the composites after thermal aging treatments were investigated. The mapping relationship between damage modes and acoustic emission parameters is established by principal component analysis and k-means clustering methods, then the acoustic emission data were trained by K-Nearest Neighbor algorithms to obtain a damage modes identification model with positive predictive values above 90%. The results showed that more matrix cracking signals are captured (or matrix cracking accumulation acoustic emission (AE) energy is abruptly raised) at the early stage of loading after 32 days of thermal aging. With the addition of multilayer graphene, the fiber/matrix debonding accumulation AE energy is significantly less than the matrix cracking accumulation AE energy, which is more obvious after thermal aging. Damage initiation and extension obtained by the acoustic emission events help to find the correlation between aging time and interior interface damage mechanism.

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Acoustic emission data based deep learning approach for classification and detection of damage-sources in a composite panel

Cited by 84 publications

References 34 publications

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Deep Learning Approach for Damage Classification Based on Acoustic Emission Data in Composite Materials

Damage Progress Classification in AlSi10Mg SLM Specimens by Convolutional Neural Network and k-Fold Cross Validation

Effects of thermal aging on damage evolution behavior of glass fiber reinforced composites with multilayer graphene by acoustic emission

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