Study on Delamination Damage of CFRP Laminates Based on Acoustic Emission and Micro Visualization

Li, Wei; Liu, Yinghonglin; Jiang, Peng; Fu-ping, Guo; Cheng, Jiahao

doi:10.3390/ma15041483

Cited by 11 publications

(6 citation statements)

References 56 publications

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“…Oftentimes, two or more failure modes can develop together, leading to failure complexity or unpredictability. In most research work, fiber breakage, matrix cracking, and delamination have been studied as the main damage mechanisms [5,[53][54][55]. The main objective of this paper is to apply a deep learning method for the damage classification of composite materials.…”

Section: Materials Preparation and Test Proceduresmentioning

confidence: 99%

“…During the test, observation and recording of the damage pattern, loading curve and experimental phenomena during the specimen failure were carried out. In order to control the bending stiffness of the thin plate to within a certain range, the combined effects of bending and torsion coupling, cantilever stiffness, and thermal residual stress were considered [5,59]. The delamination test specimen was selected from the carbon fiber composite laminate with the model number T700, and the laying mode of laminate was [0 • ] 24 and had the dimensions 175 mm × 25 mm × 4 mm.…”

Section: Materials Preparation and Test Proceduresmentioning

confidence: 99%

“…However, variable operational and loading conditions may lead to damage, which can significantly jeopardize the safety of the structural system. Complicated failure mechanisms are distinct features of carbon fiber composites, and generally include intralaminar fiber breakage, matrix cracking, fiber/matrix interface debonding, and interlaminar delamination [4][5][6]. As composites experience deformation, a variety of damage modes interact with each other [7][8][9] and it is difficult to determine the different damage mode stages in composite materials.…”

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: Materials Preparation and Test Proceduresmentioning

confidence: 99%

Section: Materials Preparation and Test Proceduresmentioning

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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“…A non-destructive testing method called Acoustic Emission (AE) can be used to identify the beginning and progression of delamination in sandwich structures [1,14]. AE entails monitoring the sandwich structure's high-frequency acoustic waves while it is stressed or deformed.…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based prediction of delamination growth in composite structures: bayesian optimization and hyperparameter refinement

Demircioğlu,

Bakır

2023

Phys. Scr.

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In this study, a novel method for detecting the growth of delamination in sandwich structures has been proposed. To this end, we suggested hybridizing the Deep Learning techniques (DL) and Finite Element Method (FEM) for predicting the growth of delamination in this structures. A dataset of simulated delamination growth under different delamination sizes has been produced using the FEM method. Then, a DL model has been trained using this dataset to precisely predict the growth of delamination. This study focused on predicting delamination growth using a tuned and optimized deep learning based regressor. Therefore, to find the ideal set of hyperparameters, the Bayesian optimization algorithm has been used for selecting the best structure and enhancing the regressor performance. Afterward, the model was evaluated and multiple processes were conducted to improve its behavior and solve its stability and overfitting issues. Particularly, an inconsistency between validation loss and training loss has been initially detected in the behaviour of the model, which may indicate overfitting. To tackle this issue, dropout regularization has been added, which improved the consistency between the loss functions but results in less smooth convergence from the expectations. So, in a third study, dropout and L1 regularization has been combined to improve the stability of the model. This combination achieved a consistent and smooth convergence between the validation and training loss functions. The findings highlight the importance of hyperparameter optimization and regularization techniques in improving regression model performance. The study shows the efficiency of Bayesian optimization in hyperparameter tuning and the iterative optimization of a regression model. Furthermore, the outcomes show that the suggested method can identify and predict delamination growth with high accuracy.

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“…The results showed that matrix cracking resulted in higher frequency values whereas delamination resulted in lower frequency values. Li et al [19] utilised two AE sensors to investigate the influence of delamination length in composite cantilever beam on rise angle and strain energy release rate of the waveforms. The results showed that both parameters can be effectively used in delamination detection.…”

Section: Introductionmentioning

confidence: 99%

Delamination Detection in Thin-Walled Composite Structures Using Acoustic Pitch-Catch Technique With Fiber Bragg Grating Sensors

2023

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Structural health monitoring (SHM) of a composite structure is essential in maintaining the integrity of the structure. Over the years, various studies have reported on the use of conventional electrical sensors in analysing acoustic wave propagation for delamination detection. However, electrical sensors are associated with drawbacks such as high signal attenuation, are prone to electromagnetic interference (EMI) and are not suitable for harsh environments. Therefore, this paper reported on the use of fiber Bragg grating (FBG) sensors for delamination detection. Two composite structures with delamination sizes of 10 cm × 2 cm and 10 cm × 6 cm were fabricated. Two FBGs were bonded before and after the delamination. In addition, three trials of impacts were induced at the centre of the structure. Multiple signal parameters were obtained and analysed, which were the time delay, amplitude difference and velocity difference. The experimental results revealed that the time delay, amplitude and velocity analysis varied for both the delamination sizes with an average percentage of 42.36%, 97.09% and 42.39%, respectively. Therefore, it was confirmed that the increase in delamination size resulted in a longer time delay, higher signal amplitude attenuation and slower wave propagation.

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Study on Delamination Damage of CFRP Laminates Based on Acoustic Emission and Micro Visualization

Cited by 11 publications

References 56 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

Deep learning-based prediction of delamination growth in composite structures: bayesian optimization and hyperparameter refinement

Delamination Detection in Thin-Walled Composite Structures Using Acoustic Pitch-Catch Technique With Fiber Bragg Grating Sensors

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