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

Rodríguez-Martín, Manuel; Fueyo, José G; Gonzalez-Aguilera, Diego; Madruga, Francisco J; García-Martín, Roberto; Muñóz, Ángel Luis; Pisonero, Javier

Rodríguez-Martín, Manuel; Fueyo, José G; Gonzalez-Aguilera, Diego; Madruga, Francisco J; García-Martín, Roberto; Muñóz, Ángel Luis; Pisonero, Javier.

Afiliação

Rodríguez-Martín M; Department of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, Spain.
Fueyo JG; Department of Technology, Universidad Católica de Ávila, 05005 Ávila, Spain.
Gonzalez-Aguilera D; Department of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, Spain.
Madruga FJ; Department of Cartographic and Land Engineering, Universidad de Salamanca, 05003 Ávila, Spain.
García-Martín R; Photonics Engineering Group, CIBER-BBN and IDIVAL, Universidad de Cantabria, 39005 Santander, Cantabria, Spain.
Muñóz ÁL; Department of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, Spain.
Pisonero J; Department of Cartographic and Land Engineering, Universidad de Salamanca, 05003 Ávila, Spain.

Sensors (Basel) ; 20(14)2020 Jul 17.

Article em En | MEDLINE | ID: mdl-32709017

Palavras-chave

active thermography (AT); additive materials (AM); finite element method (FEM); machine learning (ML); non-destructive testing (NDT); quality assessment (QA)

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article

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Texto completo: 1 Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2020 Tipo de documento: Article