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

Tao, Bo; Wang, Yan; Qian, Xinbo; Tong, Xiliang; He, Fuqiang; Yao, Weiping; Chen, Bin; Chen, Baojia.

Afiliação

Tao B; Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
Wang Y; Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan, China.
Qian X; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, China.
Tong X; Precision Manufacturing Institute, Wuhan University of Science and Technology, Wuhan, China.
He F; Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.
Yao W; Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.
Chen B; Research Center for Biomimetic Robot and Intelligent Measurement and Control, Wuhan University of Science and Technology, Wuhan, China.
Chen B; Hubei Key Laboratory of Hydroelectric Machinery Design and Maintenance, China Three Gorges University, CTGU, Yichang, China.

Front Bioeng Biotechnol ; 10: 818112, 2022.

Article em En | MEDLINE | ID: mdl-35387296

Palavras-chave

deep learning; digital photoelasticity; inverse problem solving; neural network; stress evaluation

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Bioeng Biotechnol Ano de publicação: 2022 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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