Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Torabi, Keivan; Samadi, Mostafa; Heidari-Rarani, M.

doi:10.1177/0731684417703489

Cited by 7 publications

(3 citation statements)

References 26 publications

(46 reference statements)

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“…1 These sensors are applied to some special composite structures for signal collecting, such as rectangular plates and beams. 35,36 However, the sensors cannot behave well when it comes to a spherical composite structure since the inflated capsule has lower stiffness as well as weaker vibration responses than the aforementioned structures under applied excitation. To address the problem, a novel scheme of stress–strain-based vibration response measurement for spherical composite structures is proposed.…”

Section: Damage Detection Of Composite Structures Using Stress–strain...mentioning

confidence: 99%

Stress–strain-based crack damage detection of composite structures using selective kernel convolutional networks and continuous wavelet transform

Chen

Zhu

et al. 2022

Structural Health Monitoring

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Composite structures are widely used due to their excellent performance. To improve their safety and reliability, non-destructive testing (NDT) methods are implemented to achieve efficient damage detection. In this paper, a novel stress–strain-based damage detection approach is proposed for composite structures by using continuous wavelet transform (CWT) and selective kernel convolutional network (SKNet), which exhibit good robustness when dealing with the stress–strain signals collected from different positions of composite structures. First, a stress–strain-based measuring scheme is designed for vibration response monitoring of the composite structures. Second, the collected stress–strain signals are converted into two-dimensional time–frequency images by the CWT. Then, the SKNet is constructed to classify these images for damage detection. Finally, an experimental test is conducted on a composite capsule, data from which is employed to evaluate the proposed method. The comparison results of our proposed method and other state-of-the-art approaches demonstrate its superiority.

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Section: Damage Detection Of Composite Structures Using Stress–strain...mentioning

confidence: 99%

Stress–strain-based crack damage detection of composite structures using selective kernel convolutional networks and continuous wavelet transform

Chen

Zhu

et al. 2022

Structural Health Monitoring

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“…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. Consequently, it is necessary to perform damage onset detection, source localization, damage classification, and premature failure predictions on composite structures.…”

Section: Introductionmentioning

confidence: 99%

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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“…For the sake of simple and efficient methods, which are within reach of everyone and in order to avoid making false assumptions, following complicate experimental techniques or using tedious theoretical methods, some innovative ideas were proposed to simplify the delamination tracking process. In a recent study, Torabi et al [29] proposed a new practical procedure for detecting delamination in beam-like composite structures. This technique is based on having a movable simple support along the beam's length which permits accurate identification of the axial location and length of the delamination based on the first natural frequency only.…”

Section: Introductionmentioning

confidence: 99%

A simplified modal-based method to quantify delamination in carbon fibre-reinforced plastic beam

Khammassi

Wali

Al-Mutory

et al. 2019

Nondestructive Testing and Evaluation

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This study contributes to our knowledge about the detection and recognition of carbon fibre-reinforced plastic (CFRP) composites by using a modal analysis technique. Delamination was artificially induced during the manufacturing process of CFRP beams so that the dimension and location of the artificial delamination were known. The first three longitudinal bending modes were considered for detecting, locating, and evaluating the delamination. Contrary to what is commonly practised, the sensor placed on the CFRP structure had a large mass in order to contribute actively to restricting the delamination and hence modifying the structure's natural frequencies, thus making it much easier to monitor its health condition. The technique was implemented on a beam with a delamination gap of 9% of the beam's length, positioned within the span of the beam. The results demonstrate that the proposed technique can be effective not only to locate the delamination but also to measure its extent accurately.

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Moving support technique for delaminatoin detection in laminated composite beams using the first natural frequency

Cited by 7 publications

References 26 publications

Stress–strain-based crack damage detection of composite structures using selective kernel convolutional networks and continuous wavelet transform

Stress–strain-based crack damage detection of composite structures using selective kernel convolutional networks and continuous wavelet transform

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

A simplified modal-based method to quantify delamination in carbon fibre-reinforced plastic beam

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