Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

Liu, Guozeng; Gao, Weicheng; Liu, Wei; Wei, Yanze; Zou, Xionghui; Bai, Weiliang; Chen, Peng

doi:10.1080/10589759.2023.2284248

Nondestructive Testing and Evaluation

2023

DOI: 10.1080/10589759.2023.2284248

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Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

Guozeng Liu,

Weicheng Gao,

Wei Liu

et al.

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2024

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Cited by 2 publications

References 36 publications

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Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Deng,

Liu,

Cao

et al. 2024

Composite Structures

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Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Deng,

Liu,

Cao

et al. 2024

Composite Structures

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A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

Liu,

Ren,

Tian

2024

Sensors

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In the assessment of pipeline integrity using magnetic flux leakage (MFL) detection, it is crucial to quantify defects accurately and efficiently using MFL signals. However, in complex detection environments, traditional defect inversion methods exhibit low quantification accuracy and efficiency due to the complexity of their algorithms or excessive reliance on a priori knowledge and expert experience. To address these issues, this study presents a novel defect quantification method based on multi-sensor signal fusion (MSSF). The method employs a multi-sensor probe to fuse the MFL signals under multiple lift-off values, enhancing the diversity of defect information. This enables defect-opening profile recognition using the characteristic approximation approach (CAA). Subsequently, the MSSF method is based on a 3D magnetic dipole model and integrates the structural features of multi-sensor probes to develop an algorithm. This algorithm iteratively determines the defect depth at multiple data acquisition points within the defect region to obtain the maximum defect depth. The feasibility of the MSSF quantification method is validated through finite element simulation and physical experiments. The results demonstrate that the proposed method achieves accurate defect quantification while enhancing efficiency, with the number of iterations for each defect depth calculation point consistently requiring fewer than 15 iterations. For rectangular metal loss, perforation, and conical defects, quantification errors are less than 10%, meeting practical inspection requirements.

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Low-velocity impact damage detection in CFRP composites by applying long pulsed thermography based on post-processing techniques

Cited by 2 publications

References 36 publications

Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

A Novel Defect Quantification Method Utilizing Multi-Sensor Magnetic Flux Leakage Signal Fusion

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