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Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network

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 o...

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Autores principales: Tao, Bo, Wang, Yan, Qian, Xinbo, Tong, Xiliang, He, Fuqiang, Yao, Weiping, Chen, Bin, Chen, Baojia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978563/
https://www.ncbi.nlm.nih.gov/pubmed/35387296
http://dx.doi.org/10.3389/fbioe.2022.818112
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author Tao, Bo
Wang, Yan
Qian, Xinbo
Tong, Xiliang
He, Fuqiang
Yao, Weiping
Chen, Bin
Chen, Baojia
author_facet Tao, Bo
Wang, Yan
Qian, Xinbo
Tong, Xiliang
He, Fuqiang
Yao, Weiping
Chen, Bin
Chen, Baojia
author_sort Tao, Bo
collection PubMed
description 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.
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spelling pubmed-89785632022-04-05 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 Front Bioeng Biotechnol Bioengineering and Biotechnology 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. Frontiers Media S.A. 2022-03-21 /pmc/articles/PMC8978563/ /pubmed/35387296 http://dx.doi.org/10.3389/fbioe.2022.818112 Text en Copyright © 2022 Tao, Wang, Qian, Tong, He, Yao, Chen and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Tao, Bo
Wang, Yan
Qian, Xinbo
Tong, Xiliang
He, Fuqiang
Yao, Weiping
Chen, Bin
Chen, Baojia
Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title_full Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title_fullStr Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title_full_unstemmed Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title_short Photoelastic Stress Field Recovery Using Deep Convolutional Neural Network
title_sort photoelastic stress field recovery using deep convolutional neural network
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8978563/
https://www.ncbi.nlm.nih.gov/pubmed/35387296
http://dx.doi.org/10.3389/fbioe.2022.818112
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