Tao Bo, Wang Yan, Qian Xinbo, Tong Xiliang, He Fuqiang, Yao Weiping, Chen Bin, Chen Baojia
Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.
Front Bioeng Biotechnol. 2022 Mar 21;10:818112. doi: 10.3389/fbioe.2022.818112. eCollection 2022.
Recent work has shown that deep convolutional neural network is capable of solving inverse problems in computational imaging, and recovering the stress field of the loaded object from the photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of the fringe pattern is affected by the geometry of the specimen and experimental configuration. When the loaded object produces complex fringe distribution, the traditional stress analysis methods still face difficulty in unwrapping. In this study, a deep convolutional neural network based on the encoder-decoder structure is proposed, which can accurately decode stress distribution information from complex photoelastic fringe images generated under different experimental configurations. The proposed method is validated on a synthetic dataset, and the quality of stress distribution images generated by the network model is evaluated using mean squared error (MSE), structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), and other evaluation indexes. The results show that the proposed stress recovery network can achieve an average performance of more than 0.99 on the SSIM.
近期的研究表明，深度卷积神经网络能够解决计算成像中的逆问题，并且从光弹性条纹图案中恢复加载物体的应力场也可被视为一个逆问题求解过程。然而，条纹图案的形成受试样几何形状和实验配置的影响。当加载物体产生复杂的条纹分布时，传统的应力分析方法在去包裹方面仍面临困难。在本研究中，提出了一种基于编码器 - 解码器结构的深度卷积神经网络，它能够从在不同实验配置下生成的复杂光弹性条纹图像中准确解码应力分布信息。该方法在合成数据集上得到验证，并使用均方误差（MSE）、结构相似性指数测量（SSIM）、峰值信噪比（PSNR）等评估指标对网络模型生成的应力分布图像质量进行评估。结果表明，所提出的应力恢复网络在SSIM上能够实现超过0.99的平均性能。
