Empowering materials processing and performance from data and AI
Resumen: [No abstract available]
Idioma: Inglés
DOI: 10.3390/ma14164409
Año: 2021
Publicado en: Materials 14, 16 (2021), 4409 [4 pp]
ISSN: 1996-1944

Factor impacto JCR: 3.748 (2021)
Categ. JCR: METALLURGY & METALLURGICAL ENGINEERING rank: 18 / 79 = 0.228 (2021) - Q1 - T1
Categ. JCR: PHYSICS, CONDENSED MATTER rank: 28 / 69 = 0.406 (2021) - Q2 - T2
Categ. JCR: PHYSICS, APPLIED rank: 56 / 161 = 0.348 (2021) - Q2 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 177 / 345 = 0.513 (2021) - Q3 - T2
Categ. JCR: CHEMISTRY, PHYSICAL rank: 85 / 165 = 0.515 (2021) - Q3 - T2

Factor impacto CITESCORE: 4.7 - Materials Science (Q2)

Factor impacto SCIMAGO: 0.604 - Materials Science (miscellaneous) (Q2) - Condensed Matter Physics (Q2)

Tipo y forma: (Published version)
Área (Departamento): Área Mec.Med.Cont. y Teor.Est. (Dpto. Ingeniería Mecánica)

Creative Commons You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.


Exportado de SIDERAL (2023-05-18-15:44:00)


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Este artículo se encuentra en las siguientes colecciones:
Articles > Artículos por área > Mec. de Medios Contínuos y Teor. de Estructuras



 Record created 2022-07-05, last modified 2023-05-19


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