Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network - Tao, Bo; Wang, Yan; Qian, Xinbo; Tong, Xiliang; He, Fuqiang; Yao, Weiping; Chen, Bin; Chen, Baojia (Frontiers in Bioengineering and Biotechnology, 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 loaded object from photoelastic fringe pattern can also be regarded as an inverse problem solving process. However, the formation of 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 have difficulties in unwrapping. In this paper, a deep convolutional neural network based on 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 above 0.99 on the SSIM.