.png){kind=icon}
返回
Damage identification in sandwich structures using Convolutional Neural Networks
.png){kind=icon}
PRE
AI
DOI：10.1016/j.ymssp.2024.111649.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The increasing complexity of structures and materials, coupled with ever more stringent demands for safety and cost reduction in maintenance operations, has driven the need to develop advanced techniques for structural integrity monitoring, known as Structural Health Monitoring (SHM). In this context, this study investigates the use of image processing techniques of mode shapes in a composite sandwich panel, employing various Convolutional Neural Network (CNN) models, with the purpose of identifying damage. The main objective is to classify the type of damage, whether it is in the core, at the interface, or in the laminate, followed by the precise location of the flaw and determination of damage dimensions in terms of length and width. To achieve these goals, the finite element method was used to create a representative database, and subsequently, an efficient data management system and model implementation were established to optimize computational costs. The results obtained show that, in the classification task, a high accuracy in damage type identification (99%) was achieved, even when images had high levels of noise. Regarding the regression task for damage localization, satisfactory results were obtained, while the dimensioning of damage length proved acceptable, albeit with some limitations, and inclination identification presented challenges. This study represents a significant advancement in the field of SHM and explores both the advantages and limitations of using Convolutional Neural Networks for this purpose. The results obtained provide valuable insights for the practical application of these techniques in the detection and assessment of damage in sandwich structures, thus contributing to enhancing the safety and efficiency of maintaining such structures.
Keyword:
Sandwich structures
Structural integrity monitoring
Convolutional Neural Networks
Image processing
期刊
IF:
8.9
论文数:
1.3W
被引数:
6.6W
机构
暂无机构信息
引用论文
Delamination identification in sandwich composite structures using machine learning techniques使用机器学习技术在夹层复合材料结构中的分层识别
PREAI
分享收藏Gradient-based learning applied to document recognition基于梯度的学习在文档识别中的应用.png)
PROCEEDINGS OF THE IEEE
PROCEEDINGS OF THE IEEEIF25.9
PREAI
分享收藏SciPy 1.0: fundamental algorithms for scientific computing in PythonSciPy 1.0: Python中科学计算的基本算法NATURE METHODS
OAAI
NATURE METHODSIF32.1
OAAI分享收藏Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks基于一维卷积神经网络的实时振动结构损伤检测
PREAI
分享收藏 没有更多内容
没有更多内容