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- Title
A Deep Learning-Based Ultrasonic Diffraction Data Analysis Method for Accurate Automatic Crack Sizing.
- Authors
Fei, Qinnan; Cao, Jiancheng; Xu, Wanli; Jiang, Linzhao; Zhang, Jun; Ding, Hui; Yan, Jingli
- Abstract
Featured Application: This study belongs to the field of intelligent non-destructive testing and proposes a CNN-based method for accurate quantification of defect characterized by ultrasonic diffraction method. The innovation in this study lies in several key aspects: (1) Development of a CNN architecture that automatically classifies A-scan signals in the scan path; (2) Connectivity region solving algorithm to accurately calculate the defect-region size according to the classification results; (3) An intelligent noise reduction framework that enables accurate identification of defective areas in complex noise situations. The purpose of this paper is to automate the interpretation of data during ultrasonic diffraction using a non-destructive testing (NDT) technique to accurately size defects for assisting in decision-making. A convolutional neural network (CNN) architecture was developed to automatically measure the length of the defect. Using the architecture, the population of A-scan signals in the scanning path was classified. The defect region was extracted and its size in the scanning direction was obtained by the connected region solution algorithm based on the classification results. The arrival time of diffraction waves was accurately identified by the intelligent denoising framework proposed, combined with Hilbert transform, and then the height of defects was calculated by corresponding geometric relations. The estimation results demonstrate that the measurement method can be considered as a useful technique for crack sizing in industrial structures, even in the case of complex noise.
- Subjects
CONVOLUTIONAL neural networks; NONDESTRUCTIVE testing; ULTRASONICS; HILBERT transform; DATA analysis; DEEP learning
- Publication
Applied Sciences (2076-3417), 2024, Vol 14, Issue 11, p4619
- ISSN
2076-3417
- Publication type
Article
- DOI
10.3390/app14114619