The applied of self-organizing clustering analysis on Coin-tap Test system of airplane composite structure

Xu, Zhenteng; Li, Yanjun; Zhao, Suyang; Ma, Anxiang; Qiao, Lei; Wang, Lei

doi:10.1109/phm.2014.6988194

Cited by 4 publications

(2 citation statements)

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“…Subsequently, researchers improved the original theory and established more detailed and comprehensive detection procedures for aviation composite materials. [21][22][23][24] In recent years, a few researchers use the concept of percussion-based method to detect fatigue cracks of metal materials and bolt looseness in structural health monitoring. 25,26 For timber structures, engineers have used tapping sound to identify the health condition of the historical timber structures for many decades; however, the current reported tapping methods for timber damage diagnosis are mainly relied on the experiences of test personnel.…”

Section: Introductionmentioning

confidence: 99%

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

Chen

Xiong

Sang

et al. 2021

Structural Health Monitoring

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Timber structures have been a dominant form of construction throughout most of history and continued to serve as a widely used staple of civil infrastructure in the modern era. As a natural material, wood is prone to termite damages, which often cause internal cavities for timber structures. Since internal cavities are invisible and greatly weaken structural load-bearing capacity, an effective method to timber internal cavity detection is of great importance to ensure structural safety. This article proposes an innovative deep neural network (DNN)–based approach for internal cavity detection of timber columns using percussion sound. The influence mechanism of percussion sound with the volume change of internal cavity was studied through theoretical and numerical analysis. A series of percussion tests on timber column specimens with different cavity volumes and environmental variations were conducted to validate the feasibility of the proposed DNN-based approach. Experimental results show high accuracy and generality for cavity severity identification regardless of percussion location, column section shape, and environmental effects, implying great potentials of the proposed approach as a fast tool for determining internal cavity of timber structures in field applications.

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Section: Introductionmentioning

confidence: 99%

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

Chen

Xiong

Sang

et al. 2021

Structural Health Monitoring

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“…This approach has been improved upon and built into several commercial instruments, including the woodpecker (Mitsui Heavy Industries), the rapid damage detection device (Boeing), and the computer aided tap tester (Iowa State University) [6]. More sophisticated approaches, including the use of neural nets [7] and cluster analysis [8], have been used for analyzing the response of such instruments. In a review of the tap test for defect detection of sandwich panels, the Federal Aeronautics Administration (FAA) found that the results were highly variable [9].…”

Section: Introductionmentioning

confidence: 99%

Eddy Current Array Inspection of Damaged CFRP Sandwich Panels

Underhill

Rellinger

Krause

et al. 2020

Journal of Nondestructive Evaluation, Diagnostics and Prognostics of Engineering Systems

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Abstract The use of eddy current (EC) arrays to detect damage in sandwich panels, such as disbonding of the carbon fiber reinforced polymer (CFRP) face-sheet to the core, is investigated. It is shown that the array is very sensitive to slight core crush and can readily find small dents and disbonds. At the same time, the eddy current array can look much deeper into the honeycomb to detect defects such as tears. The phase map of the EC signal can be used in some cases to distinguish between different types of damage. EC arrays offer the ability to rapidly scan large areas of CFRP panels.

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Damage Detection in Composite Materials Using Tap Test Technique and Neural Networks

et al. 2021

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The applied of self-organizing clustering analysis on Coin-tap Test system of airplane composite structure

Cited by 4 publications

References 0 publications

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

An innovative deep neural network–based approach for internal cavity detection of timber columns using percussion sound

Eddy Current Array Inspection of Damaged CFRP Sandwich Panels

Damage Detection in Composite Materials Using Tap Test Technique and Neural Networks

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