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论文:2023,Vol:41,Issue(5):932-941 |
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引用本文： |
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李坤鹏, 李彪, 张彦军, 周颜, 张腾, 李亚智. 基于应变监控数据的金属结构疲劳裂纹量化模型研究[J]. 西北工业大学学报 |
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LI Kunpeng, LI Biao, ZHANG Yanjun, ZHOU Yan, ZHANG Teng, LI Yazhi. A fatigue crack quantification model for metallic structure based on strain monitoring data[J]. Journal of Northwestern Polytechnical University |
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| 基于应变监控数据的金属结构疲劳裂纹量化模型研究 |
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| 李坤鹏1, 李彪1, 张彦军2, 周颜2, 张腾3, 李亚智1 |
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1. 西北工业大学 航空学院, 陕西 西安 710072;
2. 航空工业第一飞机设计研究院强度设计研究所, 陕西 西安 710089;
3. 空军工程大学 航空工程学院, 陕西 西安 710038 |
| 摘要: |
| 实时获取金属结构的疲劳裂纹长度是开展飞机单机寿命监控和剩余寿命估算的基础。采用深度学习方法,提出了一种基于应变监控数据的金属结构疲劳裂纹长度预测模型,通过构造循环对抗网络模型、裂纹尺寸的分类模型和裂纹长度的量化模型,分别实现了含裂纹结构的应变试验数据与有限元模型数据的映射、裂纹尺寸范围的准确分类、裂纹长度的精确量化。将上述方法应用于中心带孔金属板在随机载荷谱下的疲劳裂纹监测,有效实现了疲劳裂纹长度的实时预测。与试验结果对比表明,单孔板的孔边疲劳裂纹长度预测误差小于1 mm,满足工程实际的需求。 |
| 关键词:
疲劳裂纹
应变监测
深度学习
数据驱动模型
疲劳裂纹预测
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| A fatigue crack quantification model for metallic structure based on strain monitoring data |
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| LI Kunpeng1, LI Biao1, ZHANG Yanjun2, ZHOU Yan2, ZHANG Teng3, LI Yazhi1 |
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1. School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Department of Strength Design, AVIC the First Aircraft Institute, Xi'an 710089, China;
3. School of Aeronautic Engineering, Air Force Engineering University, Xi'an 710038, China |
| Abstract: |
| Obtaining the real-time fatigue crack length of a metallic structure is the prerequisite of the fatigue life monitoring and residual life estimation for an aircraft. This paper proposed a metallic structure's fatigue crack prediction model using strain monitoring data based on deep learning method. A cycle consistent adversarial network was developed to map the strain monitoring data from experimental measurement with those from finite element modeling. A crack size classification model and a crack length quantification model were proposed to classify the crack size range and identify the exact crack length, respectively. The proposed model was applied to predict the fatigue crack growth in centeral hole metallic plates subjected to random loading spectrum. The results showed that the prediction is effective and accurate. |
| Key words:
fatigue crack
strain monitoring
deep learning
data driven model
fatigue crack prediction
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| 收稿日期: 2022-10-14
修回日期:
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| DOI: 10.1051/jnwpu/20234150932 |
| 基金项目: 国家自然科学基金(12072272,52005507)与国家级重点实验室基金(2022-xxxx-LB-020-04,HTKJ2021KL011003)资助 |
| 通讯作者: 李彪(1987—),西北工业大学副教授,主要从事航空结构疲劳与断裂研究。e-mail:libiao@nwpu.edu.cn
Email：libiao@nwpu.edu.cn |
| 作者简介: 李坤鹏(1996—),西北工业大学硕士研究生,主要从事航空结构疲劳与断裂研究。
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| 作者相关文章 |
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| 李坤鹏 在本刊中的所有文章 |
| 李彪 在本刊中的所有文章 |
| 张彦军 在本刊中的所有文章 |
| 周颜 在本刊中的所有文章 |
| 张腾 在本刊中的所有文章 |
| 李亚智 在本刊中的所有文章 |
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