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

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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Bibliographic Details
Main Authors: Manuel Rodríguez-Martín, José G. Fueyo, Diego Gonzalez-Aguilera, Francisco J. Madruga, Roberto García-Martín, Ángel Luis Muñóz, Javier Pisonero
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
active thermography (AT)
finite element method (FEM)
non-destructive testing (NDT)
quality assessment (QA)
machine learning (ML)
additive materials (AM)
Online Access:https://www.mdpi.com/1424-8220/20/14/3982

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