Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr-Cr<sub>3</sub>C<sub>2</sub> Coatings Based on a Hierarchical Neural Network.

Gui, Longen; Wang, Botong; Cai, Renye; Yu, Zexin; Liu, Meimei; Zhu, Qixin; Xie, Yingchun; Liu, Shaowu; Killinger, Andreas

Prediction of In-Flight Particle Properties and Mechanical Performances of HVOF-Sprayed NiCr-Cr₃C₂ Coatings Based on a Hierarchical Neural Network.

Gui, Longen; Wang, Botong; Cai, Renye; Yu, Zexin; Liu, Meimei; Zhu, Qixin; Xie, Yingchun; Liu, Shaowu; Killinger, Andreas.

Afiliación

Gui L; School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.
Wang B; National Engineering Laboratory for Modern Materials Surface Engineering Technology, Institute of New Materials, Guangdong Academy of Science, Guangzhou 510650, China.
Cai R; School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.
Yu Z; National Engineering Laboratory for Modern Materials Surface Engineering Technology, Institute of New Materials, Guangdong Academy of Science, Guangzhou 510650, China.
Liu M; School of Automobile and Transportation Engineering, Guangdong Polytechnic Normal University, Guangzhou 510665, China.
Zhu Q; School of Mechanical and Electrical Engineering, Soochow University, Suzhou 215137, China.
Xie Y; Institute for Manufacturing Technologies of Ceramic Components and Composites (IMTCCC), University of Stuttgart, Allmandring 7b, 70569 Stuttgart, Germany.
Liu S; School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.
Killinger A; School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou 215009, China.

Materials (Basel) ; 16(18)2023 Sep 19.

Article en En | MEDLINE | ID: mdl-37763556

Palabras clave

NiCrCr3C2 coatings; convolutional neural network; high-velocity oxygen fuel spray; machine learning; physical information neural network

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Materials (Basel) Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Suiza

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