Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods

The present article addresses a generation of predictive models that assesses the thickness and length of internal defects in additive manufacturing materials. These modes use data from the application of active transient thermography numerical simulation. In this manner, the raised procedure is an...

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Detalles Bibliográficos
Autores principales: Rodríguez-Martín, Manuel, Fueyo, José G., Gonzalez-Aguilera, Diego, Madruga, Francisco J., García-Martín, Roberto, Muñóz, Ángel Luis, Pisonero, Javier
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411725/
https://www.ncbi.nlm.nih.gov/pubmed/32709017
http://dx.doi.org/10.3390/s20143982

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7411725/
https://www.ncbi.nlm.nih.gov/pubmed/32709017
http://dx.doi.org/10.3390/s20143982

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