Damage Detection of Composite Plates by Lamb Wave Ultrasonic Tomography with a Sparse Hexagonal Network Using Damage Progression Trends

Keulen, Casey J.; Yıldız, Mehmet; Suleman, Afzal

doi:10.1155/2014/949671

Cited by 20 publications

(12 citation statements)

References 27 publications

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“…From the literature, the most common domains for the extraction of discriminative features for the pristine and damaged composite structures are frequency domain, time domain, time-frequency domain, impedance domain, and modal analysis domain [20][21][22][23][24][25]. The discriminative features in the aforementioned domains may be extracted either from low-frequency, high-wavelength structural vibration response, or high-frequency, low-wavelength guided waves or acoustic and ultrasonic waves [26][27][28][29]. Some examples of discriminative features extracted from low-frequency, highwavelength structural vibration response of composite structures are natural frequency, specific damping capacity, mode shape curvature, strain mode shapes, power spectral density, and shift in frequency response from a baseline [25,[30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Damage assessment of smart composite structures via machine learning: a review

et al. 2019

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Composite materials are heterogeneous in nature and suffer from complex non-linear modes of failure, such as delamination, matrix crack, fiber-breakage, and voids, among others. The early detection of damage in composite structures, such as airplanes, is imperative to avoid catastrophic failure and tragic consequences. This paper reports on the use of machine learning techniques for the damage assessment (i.e., detection, quantification, and localization) of smart composite structures. The success of the machine learning paradigm for damage assessment depends on the representational capability of the discriminative features for the problems of interest. However, from a practical standpoint, it is not possible to define a global or superset of discriminative features that could discriminate between damaged and undamaged states of the structures, and simultaneously make a distinction between various modes of failures. In addition, one machine learning algorithm may show optimum performance for the discriminative features of a particular problem but fails for others. This article focuses on a review of discriminative features and the corresponding machine learning algorithms (both supervised and unsupervised), for various types of damage in smart composite structures.

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Section: Introductionmentioning

confidence: 99%

Damage assessment of smart composite structures via machine learning: a review

et al. 2019

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“…Hence, continuous monitoring of the strain state of composites under different environmental conditions and mechanical loads, especially cyclic loads, is particularly important given that such loading is the most common cause inflicting catastrophic damage on composite structures during service. Therefore, it is crucial to develop techniques to monitor or “sense” the strain state of the composite during service and use these data in situ to predict the onset of failure [ 1 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring the Damage State of Fiber Reinforced Composites Using an FBG Network for Failure Prediction

et al. 2017

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A structural health monitoring (SHM) study of biaxial glass fibre-reinforced epoxy matrix composites under a constant, high strain uniaxial fatigue loading is performed using fibre Bragg grating (FBG) optical sensors embedded in composites at various locations to monitor the evolution of local strains, thereby understanding the damage mechanisms. Concurrently, the temperature changes of the samples during the fatigue test have also been monitored at the same locations. Close to fracture, significant variations in local temperatures and strains are observed, and it is shown that the variations in temperature and strain can be used to predict imminent fracture. It is noted that the latter information cannot be obtained using external strain gages, which underlines the importance of the tracking of local strains internally.

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“…Guided waves tomography showed great potential in damage imaging . Gao et al combined leave‐in‐place sensors with guided wave tomographic techniques for aircraft wing.…”

Section: Introductionmentioning

confidence: 99%

Delamination detection in composite plates by synthesizing time-reversed Lamb waves and a modified damage imaging algorithm based on RAPID

Liu

Zhong

Dong

et al. 2016

Struct. Control Health Monit.

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Combined with the modified damage imaging algorithm based on Reconstruction Algorithm for the Probabilistic Inspection of Damage (RAPID), a baseline-free detection method of the Lamb waves based on time reversal method is developed to detect the delamination in the composite plate. Through the analysis of the focus position of time reversal signal, a new calculation method of Damage Index (DI) values is proposed. The modified damage imaging algorithm based on RAPID is integrated with two image fusion methods: full summation method and full multiplication method. According to the analysis of limitations of the modified damage imaging algorithm, the transducer array is adopted, and the problem of the uneven probability distribution caused by the uneven density of sensing network is discussed. Modified methods are proposed to eliminate the influence of the uneven probability distribution and improve the accuracy and reliability of the detection results. The parameter β significantly affects the effective detection range of each sensing path in the modified damage imaging algorithm. The value of parameter β was experimentally determined and reasonably applied to detect the delamination. Based on the calculation of the DI of individual sensing path and the imaging results of delamination, the identification and localization of delamination were experimentally achieved. The imaging results demonstrated the problem of the uneven probability distribution and the validity of modified methods.

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Damage Detection of Composite Plates by Lamb Wave Ultrasonic Tomography with a Sparse Hexagonal Network Using Damage Progression Trends

Cited by 20 publications

References 27 publications

Damage assessment of smart composite structures via machine learning: a review

Damage assessment of smart composite structures via machine learning: a review

Monitoring the Damage State of Fiber Reinforced Composites Using an FBG Network for Failure Prediction

Delamination detection in composite plates by synthesizing time-reversed Lamb waves and a modified damage imaging algorithm based on RAPID

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