上海交通大学学报

上海交通大学学报

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基于时空神经网络的金属疲劳裂纹扩展预测（网络首发）

  

  1. 上海交通大学航空航天学院民机结构强度综合实验室
  • 基金资助:
    国家自然科学基金（U2241266）资助项目；

Prediction of fatigue crack growth in metal materials via spatiotemporal neural network

  1. （School of Aeronautics and Astronautics; Lab of Civil Aircraft Structures Testing, Shanghai Jiao Tong University, Shanghai 200240, China）

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摘要： 提出一种基于时空神经网络(STNN)的图像驱动模型，基于铝合金材料疲劳试验，开展了裂纹扩展预测研究。设计了边裂纹、0°裂纹角度试验件，进行了15.0%极限载荷水平的疲劳试验。通过DIC设备拍摄试件变形图片，得到5511帧位移场图像。对获得的试验图像数据进行了插值、数据增强和维度转换等，构建了STNN的数据集。分别利用卷积长短期记忆(Conv-LSTM)和SimVP两种STNN方法进行了疲劳裂纹扩展的预测，计算了其结构相似度指标(SSIM)和均方根误差(RMSE)，比较了两种方法的预测准确性。结果表明，SimVP神经网络在测试阶段的预测效果更好，该方法可以准确预测疲劳裂纹扩展速率和路径，为损伤容限分析和结构监测周期的确定提供参考。

关键词: 时空神经网络, 图像驱动, 疲劳裂纹扩展, 疲劳试验, 数字图像相关

Abstract: An image-driven model based on spatiotemporal neural network (STNN) is proposed. And prediction of crack growth is carried out based on fatigue experiments of aluminum alloy. Fatigue experiments with an initial edge crack angle of 0° and a 15.0% limit load level are designed. Images of specimen deformation are taken by DIC. And 5511 frames of displacement fields are obtained. These images are used as datasets of STNN after interpolation, augmentation and dimension-raising. Convolutional long short-term memory (Conv-LSTM) and SimVP neural networks are used to predict fatigue crack growth. The prediction accuracies of these two methods are compared in terms of the structural similarity index measure (SSIM) and the root mean square error (RMSE). The results show that SimVP neural network has better prediction performance in the test stage. This method can accurately predict fatigue crack growth rate and propagation path. And it is a reference of damage tolerance analysis and determination of inspection intervals for structures.

Key words: spatiotemporal neural network, image driven, fatigue crack growth, fatigue experiment, digital image correlation

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