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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MDPI AG
2020-07-01
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author | Manuel Rodríguez-Martín José G. Fueyo Diego Gonzalez-Aguilera Francisco J. Madruga Roberto García-Martín Ángel Luis Muñóz Javier Pisonero |
author_facet | Manuel Rodríguez-Martín José G. Fueyo Diego Gonzalez-Aguilera Francisco J. Madruga Roberto García-Martín Ángel Luis Muñóz Javier Pisonero |
author_sort | Manuel Rodríguez-Martín |
collection | DOAJ |
description | 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 different 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. |
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institution | Directory Open Access Journal |
issn | 1424-8220 |
language | English |
last_indexed | 2024-03-10T18:25:11Z |
publishDate | 2020-07-01 |
publisher | MDPI AG |
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series | Sensors |
spelling | doaj.art-e960bbb40ab74899b88b677d8e4f62c92023-11-20T07:06:42ZengMDPI AGSensors1424-82202020-07-012014398210.3390/s20143982Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning MethodsManuel Rodríguez-Martín0José G. Fueyo1Diego Gonzalez-Aguilera2Francisco J. Madruga3Roberto García-Martín4Ángel Luis Muñóz5Javier Pisonero6Department of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, SpainDepartment of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, SpainDepartment of Cartographic and Land Engineering, Universidad de Salamanca, 05003 Ávila, SpainPhotonics Engineering Group, CIBER-BBN and IDIVAL, Universidad de Cantabria, 39005 Santander, Cantabria, SpainDepartment of Mechanical Engineering, Universidad de Salamanca, 37008 Salamanca, SpainDepartment of Cartographic and Land Engineering, Universidad de Salamanca, 05003 Ávila, SpainDepartment of Cartographic and Land Engineering, Universidad de Salamanca, 05003 Ávila, SpainThe 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 different 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.https://www.mdpi.com/1424-8220/20/14/3982active thermography (AT)finite element method (FEM)non-destructive testing (NDT)quality assessment (QA)machine learning (ML)additive materials (AM) |
spellingShingle | Manuel Rodríguez-Martín José G. Fueyo Diego Gonzalez-Aguilera Francisco J. Madruga Roberto García-Martín Ángel Luis Muñóz Javier Pisonero Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods Sensors active thermography (AT) finite element method (FEM) non-destructive testing (NDT) quality assessment (QA) machine learning (ML) additive materials (AM) |
title | Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods |
title_full | Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods |
title_fullStr | Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods |
title_full_unstemmed | Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods |
title_short | Predictive Models for the Characterization of Internal Defects in Additive Materials from Active Thermography Sequences Supported by Machine Learning Methods |
title_sort | predictive models for the characterization of internal defects in additive materials from active thermography sequences supported by machine learning methods |
topic | active thermography (AT) finite element method (FEM) non-destructive testing (NDT) quality assessment (QA) machine learning (ML) additive materials (AM) |
url | https://www.mdpi.com/1424-8220/20/14/3982 |
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