Using FEM and CFD to locate cracks in compressor blades for non destructive inspections

Hameed, M. Saqib; Manarvi, Irfan Anjum

doi:10.1109/aero.2009.4839596

Cited by 4 publications

(1 citation statement)

References 10 publications

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“…Therefore, in addition to corrosion, the NDT of aircraft metal material structures mainly focuses on crack detections. NDT of such metal materials frequently uses magnetic particle inspection (MPI) technology [3,4].…”

Section: Introductionmentioning

confidence: 99%

Defect detection algorithm for magnetic particle inspection of aviation ferromagnetic parts based on improved DeepLabv3+

Zeng

Zhang

Wang³

et al. 2023

Meas. Sci. Technol.

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Non-destructive testing (NDT) of magnetic materials such as aviation parts is an indispensable part of the civil aviation maintenance industry. The NDT of such metal materials often uses magnetic particle inspection (MPI) technology. This paper proposes an improved DeepLabv3+ semantic segmentation algorithm for automatic defect detection of aviation ferromagnetic parts after MPI. In the network structure, lightweight MobileNetV2 is the backbone feature extraction network. The Dense Atrous Spatial Pyramid Pooling (DenseASPP) structure is used to strengthen feature extraction. The influence of three different DenseASPP structures on the recognition effect is compared in the experiment. At the same time, the decoder is further optimized. The experimental results show that the Ours-DeepLabv3+ network model can effectively for automatic defect detection of aviation ferromagnetic parts after MPI. The Precision, Recall, F1-score, and IOU are 81.64%, 83.12%, 82.37%, and 71.23%, respectively, which are 7.48%, 5.45%, 6.50%, and 10.1% higher than the original DeepLabv3+, and defect detail segmentation is more accurate. Compared with other semantic segmentation algorithms, this method can effectively improve the accuracy of defect detection of aviation ferromagnetic parts and meet the requirements of defect detection.

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Section: Introductionmentioning

confidence: 99%