Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves

Fenza, Angelo De; Sorrentino, Assunta; Vitiello, P.

doi:10.1016/j.compstruct.2015.07.089

Cited by 106 publications

(53 citation statements)

References 27 publications

(28 reference statements)

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“…A damage identification strategy can be developed with the aid of ANN as an efficient computing tool in composite damage simulation using FEM. [31][32][33][34] Based on the excellent nonlinear computing capability, it is expected that ANN has a great potential for quantitative damage detection in composite applications, when the accuracy of networks is effectively validated by, for instance, enough experimental data.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves

Qian

Ran

et al. 2020

Advances in Mechanical Engineering

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This article provides a quantitative nondestructive damage detection method through a Lamb wave technique assisted by an artificial neural network model for fiber-reinforced composite structures. For simulating damages with a variety of sizes, rectangular Teflon tapes with different lengths and widths are applied on a unidirectional carbon fiber–reinforced polymer composite plate. Two characteristic parameters, amplitude damage index and phase damage index, are defined to evaluate effects by the shape of the rectangular damage in the carbon fiber–reinforced polymer composite plate. The relationships between the amplitude damage index and phase damage index parameters and the damage sizes in the carbon fiber–reinforced polymer composite plate are quantitatively addressed using a three-layer artificial neural network model. It can be seen that a reasonable agreement is achieved between the pre-assigned damage lengths and widths and the corresponding predictions provided by the artificial neural network model. This shows the great potential of using the proposed artificial neural network model for quantitatively detecting the damage size in fiber-reinforced composite structures.

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Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves

Qian

Ran

et al. 2020

Advances in Mechanical Engineering

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“…For instance, Su [13] detected the actual delamination of composite laminates by using an artificial neural network, which proved the feasibility of the method used for pattern recognition in damage detection of composite materials. De [14] detected the damage location and degree of composite plates by using the combination of an artificial neural network and the method of probabilistic ellipse. Sun [15] proposed a method of damage quantification using Lamb wave based on least squares support vector machine and a genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Damage Localization of Composites Based on Difference Signal and Lamb Wave Tomography

Jiang

Liang

et al. 2020

Materials

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In order to deal with the problem of composite damage location, an imaging technique based on differential signal and Lamb wave tomography was proposed. Firstly, the feasibility of the technique put forward was verified by simulation. In this process, the composite model was regularly set down by the circular sensor array, with each sensor acting as an actuator in sequence to generate Lamb waves. Apart from that, other sensors were used to collect response signals. With regard to the damage factor, it was mainly determined by the difference between the damage signal and the non-damage signal. The probabilistic imaging algorithm was employed to carry out damage location imaging. Then, experiments were carried out so as to study the selected composite plate. Finally, the tentative outcomes have illustrated that the maximum error of damage imaging position was 7.07 mm. The relative error was 1.6%. In addition, the method has the characteristics of simple calculation and high imaging efficiency. Therefore, it has large technical potential and wide applications in the damage location and damage recognition for composite material.

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“…The purpose of this research for grain size classification is to classify an unknown object into a predefined grain group consisting of pre-classified sets of objects with similar patterns to an unknown object. For many years, it was the artificial NN that was used to detect nature patterns [5]. In that manner, the NN is capable of detecting patterns with given features.…”

Section: Introductionmentioning

confidence: 99%

A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties

Dapkus

Mažeika

Sliesoraitis³

2020

ITC

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This paper examines the performance of the commonly used neural-network-based classifiers for investigating a structural noise in metals as grain size estimation. The biggest problem which aims to identify the object structure grain size based on metal features or the object structure itself. When the structure data is obtained, a proposed feature extraction method is used to extract the feature of the object. Afterwards, the extracted features are used as the inputs for the classifiers. This research studies is focused to use basic ultrasonic sensors to obtain objects structural grain size which are used in neural network. The performance for used neural-network-based classifier is evaluated based on recognition accuracy for individual object. Also, traditional neural networks, namely convolutions and fully connected dense networks are shown as a result of grain size estimation model. To evaluate robustness property of neural networks, the original samples data is mixed for three types of grain sizes. Experimental results show that combined convolutions and fully connected dense neural networks with classifiers outperform the others single neural networks with original samples with high SN data. The Dense neural network as itself demonstrates the best robustness property when the object samples not differ from trained datasets.

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Application of Artificial Neural Networks and Probability Ellipse methods for damage detection using Lamb waves

Cited by 106 publications

References 27 publications

Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves

Application of artificial neural networks for quantitative damage detection in unidirectional composite structures based on Lamb waves

Damage Localization of Composites Based on Difference Signal and Lamb Wave Tomography

A study of supervised combined neural-network-based ultrasonic method for reconstruction of spatial distribution of material properties

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