Flaw classification and detection in thin‐plate structures based on scaled boundary finite element method and deep learning

Abstract

The identification of internal structural flaws is an important research topic in structural health monitoring. At present, structural safety inspections based on nondestructive testing procedures mainly focus on qualitative analysis; hence, it is difficult to identify the scale of flaws quantitatively. In this article, an inversion model that can realize quantitative detection is proposed by combing the scaled boundary finite element method (SBFEM) with deep learning. First, the lamb wave propagation processes in thin structures containing flaws were simulated using the SBFEM, and the echo wave signal at an observation point was recorded and paired with the flaw information. Then, the paired SBFEM datasets were used to train the deep learning model employing a convolutional neural network. A flaw classification and identification model based on SBFEM datasets was established. For a thin structure with unknown flaw information, and by using the measured observation point signals, the established deep learning model can predict flaw information in real time. Finally, the model performance was verified using several numerical examples. The results show that the proposed model can accurately predict flaw information and is robust against noise; thus, it offers an advantage over existing nondestructive testing procedures and a development in the field of structural health monitoring.

Physics‐Informed Multistage Machine Learning Strategy for the Nanomachining‐Induced Plastic Deformation Behavior

“…Some interesting works have been reported using physical signals, such as sound wave, electromagnetic wave, and temperature, which are adapted to identify structural defect and damage. [18][19][20] Here, because machining-induced plasticity behavior is highly nonlinear and has multifield coupling characteristics, traditional machine learning methods are difficult to accurately describe or predict its complex evolution process. Hence, considering its powerful data-driven and physical constraint ability, PIML strategy is adopted to investigate the machining-induced plasticity behavior for achieving fast and accurate prediction of dislocation behaviors and optimizing machining parameters with good validity and interpretability.…”

Towards Fully-automated High-performance Scaled Boundary Finite Element Analysis

“…The scaled boundary finite element method has been developed into a generalpurpose numerical method for the solution of PDE problems [5][6][7][8][9][10][11][12][13][14][15][16][17] over the last few years. This paper aims to present a scaled boundary finite element framework that automates mesh generation and is suitable to high-performance computing.…”