Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves

Ji, Baoping; Zhang, Qingdong; Cao, Jianshu; Zhang, Boyang; Zhang, Liyuan

doi:10.3390/app11020636

Cited by 8 publications

(6 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The core of the CSI is to process the source integral Equations ( 6) and (7). The inverse problem is interpreted as the minimization of the objective function, which is a linear combination of the normalized data equation and the normalized state equation.…”

Section: Scattering Problemmentioning

confidence: 99%

“…where χE i j − J j + χG D J j 2 D is the • D measure of the right side of the equation minus the left end of the equation in Equation (7), and E s j − G S J j 2 S is the • S measure of the right side of the equation minus the left residual of the Equation (6).…”

Section: Scattering Problemmentioning

confidence: 99%

“…For the detection of internal defects of living trees, the current mainstream methods include the stress wave method, ultrasound method, and computer tomography (CT) scan [6][7][8]. However, most methods have their corresponding shortcomings [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

et al. 2021

View full text Add to dashboard Cite

The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.

show abstract

Section: Scattering Problemmentioning

confidence: 99%

Section: Scattering Problemmentioning

confidence: 99%

See 1 more Smart Citation

Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Gao et al [27] proposed an algorithm of multi-frequency localized wave energy based on an LU Lamb wave to identify hidden delamination in multilayered structures. The amplitude characteristics of LU waves in different modes were explored for delamination detection in different metal-clad plates [10,28,29]. Previous research mainly analyzed the time-domain waveform and used the amplitude, time and phase of ultrasonic waves to detect and evaluate the defects.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Bond Quality of Metal-Clad Plates Using Laser Ultrasonic Local Resonance

Ji,

Cao,

Zhang

2024

Coatings

Self Cite

View full text Add to dashboard Cite

The effective detection of delamination defects, especially sub-millimeter delamination defects, in metal-clad plates is of great significance in improving product quality. In this work, the laser ultrasonic (LU) local resonance method is used to locate and characterize the sub-millimeter defects in stainless/carbon steel-clad plates. The influence of the delamination radius on the amplitude and resonant frequency of the laser ultrasound was investigated using 2D axisymmetric finite element (FE) simulations. The simulation results show that both the amplitude and the first resonance frequency (FRF) are effective features for detecting large-scale delamination defects, but the FRF is a better feature for detecting tiny delamination defects. A 304/Q235/304-clad plate specimen was made through a hot rolling bonding process, which contained a large number of self-forming delamination defects. The laser ultrasonic signals of different composite states collected in the experiment have good consistency with the simulated waveforms. The experimental results show that the laser ultrasonic local resonance method is a high-resolution imaging method, which can locate and characterize the sub-millimeter delamination defects in stainless/carbon steel-clad plates.

show abstract

“…Mainstream methods for the detection of internal defects in living tree trunks include stress wave, ultrasonic, and computed tomography (CT) scanning [1][2][3]. However, most of these methods have their drawbacks [4,5].…”

Section: Introductionmentioning

confidence: 99%

Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering

Song

Shi

Zhou

et al. 2022

Sensors

View full text Add to dashboard Cite

Trunk pests have always been one of the most important species of tree pests. Trees eroded by trunk pests will be blocked in the transport of nutrients and water and will wither and die or be broken by strong winds. Most pests are social and distributed in the form of communities inside trees. However, it is difficult to know from the outside if a tree is infected inside. A new method for the non-invasive detecting of tree interiors is proposed to identify trees eroded by trunk pests. The method is based on electromagnetic inverse scattering. The scattered field data are obtained by an electromagnetic wave receiver. A Joint-Driven algorithm is proposed to realize the electromagnetic scattered data imaging to determine the extent and location of pest erosion of the trunk. This imaging method can effectively solve the problem of unclear imaging in the xylem of living trees due to the small area of the pest community. The Joint-Driven algorithm proposed by our group can achieve accurate imaging with a ratio of pest community radius to live tree radius equal to 1:60 under the condition of noise doping. The Joint-Driven algorithm proposed in this paper reduces the time cost and computational complexity of tree internal defect detection and improves the clarity and accuracy of tree internal defect inversion images.

show abstract

Delamination Detection in Bimetallic Composite Using Laser Ultrasonic Bulk Waves

Cited by 8 publications

References 27 publications

Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

Tree Internal Defected Imaging Using Model-Driven Deep Learning Network

Evaluation of the Bond Quality of Metal-Clad Plates Using Laser Ultrasonic Local Resonance

Imaging of Insect Hole in Living Tree Trunk Based on Joint Driven Algorithm of Electromagnetic Inverse Scattering

Contact Info

Product

Resources

About