A data-driven multi-flaw detection strategy based on deep learning and boundary element method

In this article, we propose a data-driven multi-flaw detection strategy based on deep learning and the boundary element method (BEM). In the training phase, BEM is implemented to generate the database, while the block LU decomposition technique is employed to reduce the computational cost. Then the...

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Vydáno v:Computational mechanics Ročník 71; číslo 3; s. 517 - 542
Hlavní autoři: Sun, Jia, Liu, Yinghua, Yao, Zhenhan, Zheng, Xiaoping
Médium: Journal Article
Jazyk:angličtina
Vydáno: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2023
Springer
Springer Nature B.V
Témata:
Analysis
Artificial neural networks
Boundary element method
Classical and Continuum Physics
Computational Science and Engineering
Data augmentation
Deep learning
Engineering
Flaw detection
Inverse problems
Mathematical analysis
Methods
Neural networks
Original Paper
Robustness (mathematics)
Signal processing
Theoretical and Applied Mechanics
Training
Deep learning
Structures
Non-destructive testing
Boundary element method
Inverse problem
ISSN:0178-7675, 1432-0924
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Shrnutí:In this article, we propose a data-driven multi-flaw detection strategy based on deep learning and the boundary element method (BEM). In the training phase, BEM is implemented to generate the database, while the block LU decomposition technique is employed to reduce the computational cost. Then the Convolutional Neural Networks (CNNs) are adopted as a deep learning model to find the relationship between the input signals and the geometries of flaws through the training process. In the test phase, the performance of trained models will be evaluated with unseen data. As a typical inverse problem, the solution to a flaw detection problem is not always unique. In the present work, we demonstrate that such non-uniqueness is detrimental to the training process, and avoid them through some specific treatments. In order to enhance the robustness of the model, the idea of data augmentation is introduced to flaw detection tasks. The numerical results show that the presented model could produce accurate predictions in both single- and multi-flaw detection tasks with proper training. Additionally, data augmentation could significantly help against the noise.
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ISSN:0178-7675
1432-0924
DOI:10.1007/s00466-022-02231-5