|Alternative Title||Damage detection for sandwich panels with truss core based on dynamic response combining deep learning|
|Place of Conferral||北京|
|Keyword||深度学习 点阵夹层板 损伤识别 卷积神经网络|
Sandwich panels with truss core (SPTC) have the characteristics of high specific strength, high specific stiffness, thermal insulation and other versatility, and it has received more and more attention in the fields of aerospace, automotive and so on. When used in extreme environments, SPTC is partially yielded due to its own defects, which ultimately leads to structural failure and damage. Therefore, the structural health problems of SPTC have gradually attracted the attention from many experts and scholars. However, due to the complex structure of SPTC, the types of damage (debonding, cell missing, panel voids, etc.) are diverse and random, the solution is not unique and detection result is susceptible to subjective factors. The advantage of the deep learning method is that it can learn and extract features from the massive dates. There is no need for human intervention in the process, and detection results are objective. The application of deep learning method for damage detection of SPTC is expected to break through the limitations of traditional detection method. However, in order to apply deep learning to the damage identification of SPTC, it is necessary to solve the two most critical problems of dataset construction and model selection. The work in this paper is mainly focused on constructing the dataset of cell missing damage and debonding damage based on damage index, and deep learning model for damage identification should be thought.
1) The selection and optimization of deep learning algorithm for debonding damage identification is also the core of this topic. The datasets for cell missing of SPTC are trained in five models such as ZF, VGG-16 and OHEM, and a comprehensive model is found from the most suitable model. Then, by adjusting the hyperparameters the network model of a high degree is finally achieved.
2) The multiple damage indexes are constructed and the damage characteristics are characterized at the same time, which solves the problem that the single damage index is sensitive to different types of damage, and this method improves the accuracy of the damage detection. Due to the complex configuration, SPTC may have more types of damage. The single damage index does not identify all types of damage in SPTC. By using three damage indexes, the damage characteristics are simultaneously characterized and richly. The damage feature samples are generated to construct a high-quality dataset including many samples and rich features, the method fully exploits the advantages of deep learning damage recognition and trains a network model with high probability of damage identification and the method of overlap area (OLA) is applied to solve the problem of damage detection error.
3) The selected deep learning model also has a high recognition accuracy for debonding damage of SPTC. The debonding damage is the most common type of SPTC. In order to study the effects on the deep learning model for detecting debonding damage, in this paper debonding damage is simulating by using the flexible beam, three kinds of debonding damage are generated, the dataset is generated combing cell missing damage samples, and the network parameters are adjusted make the network adaptable with the dataset, and it turns out that the deep learning method can also identify debonding damage SPTC.
|王亚博. 融合深度学习的点阵夹层板动力学损伤识别方法研究[D]. 北京. 中国科学院大学,2019.|
|Files in This Item:|
|王亚博硕士论文 15611550730 （6423KB）||学位论文||开放获取||CC BY-NC-SA||Application Full Text|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.