Acoustic Emission tomography based on simultaneous algebraic reconstruction technique to visualize the damage source location in Q235B steel plate

Jiang, Yu; Xu, Feiyun; Xu, Bingsheng

doi:10.1016/j.ymssp.2015.04.013

Cited by 24 publications

(11 citation statements)

References 15 publications

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“…This approach was validated successfully on real wings of composite aircrafts. To locate and identify the damages in complex structures, Jiang et al [54] integrated the concepts of tomography and simultaneous algebraic reconstruction technique (SART). Two artificially created damages verified the efficacy of this method on the Q235B steel plate.…”

Section: Time Reversal and Artificial Neural Networkmentioning

confidence: 99%

State-of-the-Art Review on the Acoustic Emission Source Localization Techniques

Hassan¹,

Mahmood²,

Yahya

et al. 2021

IEEE Access

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The acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or without prior knowledge of wave velocity, have been presented. Several authors have conducted in-depth research in the localization of AE sources. However, existing review papers do not focus on the performance evaluation of existing source localization techniques. This paper discusses these techniques based on the number of sensors used and the geometry of the structures of interest. Furthermore, it evaluates them on the basis of their performance. At the end of this paper, a comparative analysis of existing methods has been presented based on their basic principles, key strengths, and limitations. A deep learning circular sensor cluster-based solution has the potential to provide a low-cost reliable localization solution for acoustic emission sources.

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Section: Time Reversal and Artificial Neural Networkmentioning

confidence: 99%

State-of-the-Art Review on the Acoustic Emission Source Localization Techniques

Hassan¹,

Mahmood²,

Yahya

et al. 2021

IEEE Access

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“…The AE signal at metal breakage is similar to the Hsu-Nielsen lead break signal. Therefore, experimental studies on the characteristic parameters of AE and the localization of the sources of damage often involve the simulation of material breakage and damage with a lead break experiment [14][15][16].…”

Section: Key Problems In Finite Element Analysis 21 Exciting Force Fmentioning

confidence: 99%

Signal Simulation and Experimental Research on Acoustic Emission using LS - DYNA

Jian-chao¹,

Hao²,

Zhe³

et al. 2015

JESTR

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To calculate sound wave velocity, we performed the Hsu-Nielsen lead break experiment using the ANSYS/LS-DYNA finite element software. First, we identified the key problems in the finite element analysis, such as selecting the exciting force, dividing the grid density, and setting the calculation steps. Second, we established the finite element model of the sound wave transmission in a plate under the lead break simulation. Results revealed not only the transmission characteristics of the sound wave but also the simulation and calculation of the transmission velocity of the longitudinal and transverse waves through the time travel curve of the vibration velocity of the sound wave at various nodes. Finally, the Hsu-Nielsen lead break experiment was implemented. The results of the theoretical calculation and simulation analysis were consistent with the experimental results, thus demonstrating that the research method using the ANSYS/LS-DYNA software to simulate sound wave transmissions in acoustic emission experiments is feasible and effective.

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“…In particular, structural health monitoring (SHM) strategies based on AET which is defined as a nondestructive testing method have been widely used to provide an effective way for the fault diagnosis and damage detection of materials. 52,53 Therefore, the proposed condition monitoring scheme integrating AE and t-SNE-NPSO-LSSVM can not only efficiently identify the defect categories but also diagnose the structural fault of laser cladding metallic panels. Experimental data sets are analyzed to verify the effectiveness of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Structural condition monitoring and identification of laser cladding metallic panels based on an acoustic emission signal feature optimization algorithm

Yang

2020

Structural Health Monitoring

Self Cite

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Laser cladding technology has become a central issue in industrial research and application recently. Due to the complexity of the processing and the difficulty of quantitative analysis, it is particularly important to effectively and reliably detect the forming quality of the cladding layer. Therefore, the laser cladding processing condition of metallic panels is monitored and identified based on acoustic emission technology in this article. Consequently, laser cladding acoustic emission signal of 316L stainless steel plate is first collected, and a multi-domain acoustic emission signal feature extraction method based on time–frequency domain and waveform parameters is constructed by integrating time–frequency, empirical, and inter-relationship diagraph analysis. Moreover, a feature optimization approach based on t-distributed stochastic neighbor embedding algorithm is proposed, which combines with correlation analysis to realize the de-redundancy and optimization of acoustic emission signal features. In addition, on the basis of optimizing the characteristics of acoustic emission signal, laser cladding acoustic emission signals under three technological parameters are collected. Second, a laser cladding condition identification method, which is a least square support vector machine algorithm based on niche particle swarm optimization, is proposed. The optimal parameter combination of niche particle swarm optimization algorithm is mainly selected to improve the accuracy of identification and classification. The results demonstrate the proposed t-distributed stochastic neighbor embedding feature optimization method can effectively extract the sensitive information related to the processing condition in the feature space, and the optimization of least square support vector machine parameters through niche particle swarm optimization can significantly improve the identification rate of classification. Specifically, the feasibility of the proposed t-distributed stochastic neighbor embedding–niche particle swarm optimization–least square support vector machine model to analyze laser cladding acoustic emission signal characteristics is verified, and the effectiveness of acoustic emission–based structural condition monitoring and identification of laser cladding plate-like structures is also demonstrated.

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Acoustic Emission tomography based on simultaneous algebraic reconstruction technique to visualize the damage source location in Q235B steel plate

Cited by 24 publications

References 15 publications

State-of-the-Art Review on the Acoustic Emission Source Localization Techniques

State-of-the-Art Review on the Acoustic Emission Source Localization Techniques

Signal Simulation and Experimental Research on Acoustic Emission using LS - DYNA

Structural condition monitoring and identification of laser cladding metallic panels based on an acoustic emission signal feature optimization algorithm

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