A machine learning framework for damage mechanism identification from acoustic emissions in unidirectional SiC/SiC composites

Muir, C.; Swaminathan, B.; Fields, Kirk; Almansour, Amjad S.; Sevener, K.; Smith, Craig; Presby, Michael J.; Kiser, James D.; Pollock, Tresa M.; Daly, Samantha

doi:10.1038/s41524-021-00620-7

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…Then, based on the intrinsic complex permittivity of the LCCF, the periodic structure of the fiber is designed for the light, thin, and broadband EWA absorption. Genetic algorithms (GAs) have been mainly used to optimize the mechanical properties of the materials through overall-structure optimization [44,45]. Herein, the GA is utilized to optimize the EWA performance of periodic unit links with the CST MWS.…”

Section: Resultsmentioning

confidence: 99%

Pushing the limits of microwave absorption capability of carbon fiber in fabric design based on genetic algorithm

Wang¹,

Huang²,

Yan³

et al. 2023

Journal of Advanced Ceramics

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The field of electromagnetic wave absorption (EWA) requires the adaptability, tenability, and multifunction of high-performance materials in the future. The design and preparation of EWA materials aiming at performance requirements is the latest research hotspot. Here, a performancedriven strategy for simultaneously coordinating different target performances was proposed to optimize the structure of the periodical long continuous carbon/glass fiber fabric (PCGF) materials through algorithm and simulation. The optimized structure of the PCGF not only improves the impedance matching, but also introduces the induced orientation effect for a high cooperative loss of conductivity, resonance, and periodic structure. The flexible PCGF shows a broad effective absorption bandwidth (EAB) of 32.7 GHz covering a part of the C-band and the whole X-, Ku-, K-, and Ka-bands with a thickness (d) of only 0.92 mm and a density of 5.6×10 −4 kg•cm −3 . This highly designable fabric is promising for the EWA practical application owing to integrating the characteristics of good flexibility, acid and alkali resistance, bending resistance, excellent mechanical properties, and easy large-scale preparation.

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Section: Resultsmentioning

confidence: 99%

Pushing the limits of microwave absorption capability of carbon fiber in fabric design based on genetic algorithm

Wang¹,

Huang²,

Yan³

et al. 2023

Journal of Advanced Ceramics

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“…Further, AE analysis methods combined with machine science are useful for understanding damage of composite structure. AE signals can be classified and identified according to their characteristic parameters by developing pattern classifiers [30,31], to identify the damage that occurs in the structure [32][33][34][35][36]. Common methods for pattern recognition include support vector machines (SVM), artificial neural networks (ANN), and fuzzy logic.…”

Section: Introductionmentioning

confidence: 99%

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Liu,

Liang,

Liu

et al. 2024

Mater. Res. Express

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Under the premise of ensuring safety, it is of great significance to realize the lightweight of the non-main load-bearing parts of amusement facilities. The purpose of this study is to study the failure process of a newly designed carbon fiber bumper by using acoustic emission technology. First of all, the design of carbon fiber anti-collision bar can effectively restrain passengers, and its weight is reduced by nearly two-thirds compared to traditional metal materials. Subsequently, the load-bearing capacity of the bumper was tested and acoustic emission monitoring was carried out. The test results show that this new type of combined structure of amusement facility has high reliability, which exceeds the safety factor of 3.5 required by the steel structure of amusement facility. In addition, Renyi entropy was used to select the best window function of short-time Fourier transform, and the frequency domain characteristics of acoustic emission signals of typical damage modes were discussed through appropriately selected windows function. A classifier based on supervised machine learning is established by combining frequency features and acoustic emission feature parameters. Furthermore, the use of classifiers helps to understand the damage behaviour of composite structures.

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“…AI can be helpful in mapping various parameters of AE waves to the damage parameters such as location, severity and so forth. The application of ML and deep learning algorithms to solve complex problems in the domain of structural health monitoring and fault detection has been growing in recent years [16][17][18][19][20][21][22]. Ebrahimkhanlou et al [23] utilised deep stacked encoders to determine coordinates of AE sources in an aluminium plate observing 100% accuracy for zonal localisation.…”

Section: Introductionmentioning

confidence: 99%

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

Mahajan

Banerjee

2023

Meas. Sci. Technol.

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Acoustic emission (AE) approach for non-destructive evaluation of structure is being developed for last two decades. In complex structures, one of the limitation of AE testing is to find the location of AE source. Time of flight and wave velocity are typically employed to localise AE sources. However, complex rail structure generates multiple wave modes travelling at varying speeds, making localization a difficult task. In this paper, the challenge of localisation has been split up into two parts: (i) identification of AE source zone i.e. head, web and foot (ii) identification of location along length of rail. AE events are simulated using pencil lead break (PLB) as source. Three models including artificial neural network (ANN), 1D and 2D convolutional neural network (CNN) are trained and tested using AE signals generated by PLB sources. The accuracy of zone identification is reported as 94.79% by using 2D CNN algorithm. For location classification it is also found that 2D CNN performed best with 73.12%, 79.37% and 67.50% accuracy of localizing AE source along the length in head, web and foot, respectively. For AE signal generating from actual damage in rail, bending test on inverted damaged rail section was then performed with load cases of 100kN, 150kN and 200kN. For all load cases, 2D CNN model results in accurate prediction of the zone of AE source, whereas, it accurately predicts the AE source location along the length for the load cases of higher intensity (150kN, 200kN). It is envisaged that the deep learning approach presented in this research work is expected to be helpful in developing real time monitoring system for rail inspection based on AE.

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A machine learning framework for damage mechanism identification from acoustic emissions in unidirectional SiC/SiC composites

Cited by 17 publications

References 42 publications

Pushing the limits of microwave absorption capability of carbon fiber in fabric design based on genetic algorithm

Pushing the limits of microwave absorption capability of carbon fiber in fabric design based on genetic algorithm

Applicability of a new composite amusement ride safety device based on acoustic emission monitoring technology

Acoustic emission source localisation for structural health monitoring of rail sections based on a deep learning approach

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