Predicting the weld width from high-speed successive images of the weld zone using different machine learning algorithms during laser welding.

Cai, Wang; Wang, Jian Zhuang; Cao, Long Chao; Mi, Gao Yang; Shu, Le Shi; Zhou, Qi; Jiang, Ping

Cai, Wang; Wang, Jian Zhuang; Cao, Long Chao; Mi, Gao Yang; Shu, Le Shi; Zhou, Qi; Jiang, Ping.

Afiliación

Cai W; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Wang JZ; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Cao LC; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Mi GY; School of Materials Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Shu LS; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Zhou Q; School of Aerospace Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
Jiang P; School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.

Math Biosci Eng ; 16(5): 5595-5612, 2019 06 17.

Article en En | MEDLINE | ID: mdl-31499727

Palabras clave

image processing; keyhole; laser welding; machine learning algorithm; molten pool; weld width

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Math Biosci Eng Año: 2019 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos

Texto completo

Añadir a Mi BVS

Imprimir

XML

PubMed Links

Buscar en Google