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Predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods


Abstract: 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 ad-hoc hybrid method that integrates finite element simulation and machine learning models using di_erent predictive feature sets and characteristics (i.e., regression, Gaussian regression, support vector machines, multilayer perceptron, and random forest). The performance results for each model were statistically analyzed, evaluated, and compared in terms of predictive performance, processing time, and outlier sensibility to facilitate the choice of a predictive method to obtain the thickness and length of an internal defect from thermographic monitoring. The best model to predictdefect thickness with six thermal features was interaction linear regression. To make predictive models for defect length and thickness, the best model was Gaussian process regression. However, models such as support vector machines also had significative advantages in terms of processing time and adequate performance for certain feature sets. In this way, the results showed that the predictive capability of some types of algorithms could allow for the detection and measurement of internal defects in materials produced by additive manufacturing using active thermography as a non-destructive test.



Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Rodríguez-Martín M., Fueyo J.G., Gonzalez-Aguilera D., Madruga F.J., García-Martín R., Muñóz Á.L., Pisonero J.,

 Fuente: Sensors, 2020, 20(14), 3982

Editorial: MDPI

 Fecha de publicación: 17/07/2020

Nº de páginas: 25

Tipo de publicación: Artículo de Revista

DOI: 10.3390/s20143982

ISSN: 1424-8220

Proyecto español: RTI2018-099850-B-I00

Autores/as

RODRÍGUEZ MARTÍN, MANUEL
GONZÁLEZ FUEYO, JOSÉ
DIEGO GONZALEZ AGUILERA
GARCÍA MARTÍN, ROBERTO
MUÑOZ NIETO, ÁNGEL LUIS
PISONERO CARABIAS, JAVIER