Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Anais da Academia Brasileira de Ciências (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000701701 |
Resumo: | Abstract The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available. This work solves this inverse problem with an artificial neural network that receives these experimental data as input and outputs the thermalphysical properties of the adhesive layer, where defects can occur. An autoencoder is used to reduce the dimension of the transient 1D thermography data, where its latent space represents the experimental data in a lower dimension, then these reduced data are used as input to a fully connected multilayer perceptron network. Results indicate that this is a promising approach due to the good accuracy and low computational cost observed. In addition, by including different noise levels within a defined range in the training process, the network can generalize the experimental data input and estimate the positioning of defects with similar quality. |
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Anais da Academia Brasileira de Ciências (Online) |
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Contact Failure Identification in Multilayered Media via Artificial Neural Networks and AutoencodersInverse heat conduction problemartificial neural networkscontact failure identificationapplied artificial intelligencedenoising autoencoderAbstract The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available. This work solves this inverse problem with an artificial neural network that receives these experimental data as input and outputs the thermalphysical properties of the adhesive layer, where defects can occur. An autoencoder is used to reduce the dimension of the transient 1D thermography data, where its latent space represents the experimental data in a lower dimension, then these reduced data are used as input to a fully connected multilayer perceptron network. Results indicate that this is a promising approach due to the good accuracy and low computational cost observed. In addition, by including different noise levels within a defined range in the training process, the network can generalize the experimental data input and estimate the positioning of defects with similar quality.Academia Brasileira de Ciências2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000701701Anais da Academia Brasileira de Ciências v.94 suppl.3 2022reponame:Anais da Academia Brasileira de Ciências (Online)instname:Academia Brasileira de Ciências (ABC)instacron:ABC10.1590/0001-3765202220211577info:eu-repo/semantics/openAccessJARDIM,LUCAS C.S.KNUPP,DIEGO C.DOMINGOS,ROBERTO P.ABREU,LUIZ ALBERTO S.CORONA,CARLOS C.SILVA NETO,ANTÔNIO JOSÉeng2022-07-29T00:00:00Zoai:scielo:S0001-37652022000701701Revistahttp://www.scielo.br/aabchttps://old.scielo.br/oai/scielo-oai.php||aabc@abc.org.br1678-26900001-3765opendoar:2022-07-29T00:00Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC)false |
dc.title.none.fl_str_mv |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
title |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
spellingShingle |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders JARDIM,LUCAS C.S. Inverse heat conduction problem artificial neural networks contact failure identification applied artificial intelligence denoising autoencoder |
title_short |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
title_full |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
title_fullStr |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
title_full_unstemmed |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
title_sort |
Contact Failure Identification in Multilayered Media via Artificial Neural Networks and Autoencoders |
author |
JARDIM,LUCAS C.S. |
author_facet |
JARDIM,LUCAS C.S. KNUPP,DIEGO C. DOMINGOS,ROBERTO P. ABREU,LUIZ ALBERTO S. CORONA,CARLOS C. SILVA NETO,ANTÔNIO JOSÉ |
author_role |
author |
author2 |
KNUPP,DIEGO C. DOMINGOS,ROBERTO P. ABREU,LUIZ ALBERTO S. CORONA,CARLOS C. SILVA NETO,ANTÔNIO JOSÉ |
author2_role |
author author author author author |
dc.contributor.author.fl_str_mv |
JARDIM,LUCAS C.S. KNUPP,DIEGO C. DOMINGOS,ROBERTO P. ABREU,LUIZ ALBERTO S. CORONA,CARLOS C. SILVA NETO,ANTÔNIO JOSÉ |
dc.subject.por.fl_str_mv |
Inverse heat conduction problem artificial neural networks contact failure identification applied artificial intelligence denoising autoencoder |
topic |
Inverse heat conduction problem artificial neural networks contact failure identification applied artificial intelligence denoising autoencoder |
description |
Abstract The estimation of defects positioning occurring in the interface between different materials is performed by using an artificial neural network modeled as an inverse heat conduction problem. Identifying contact failures in the bonding process of different materials is crucial in many engineering applications, ranging from manufacturing, preventive inspection and even failure diagnosis. This can be modeled as an inverse heat conduction problem in multilayered media, where thermography temperature measurements from an exposed surface of the media are available. This work solves this inverse problem with an artificial neural network that receives these experimental data as input and outputs the thermalphysical properties of the adhesive layer, where defects can occur. An autoencoder is used to reduce the dimension of the transient 1D thermography data, where its latent space represents the experimental data in a lower dimension, then these reduced data are used as input to a fully connected multilayer perceptron network. Results indicate that this is a promising approach due to the good accuracy and low computational cost observed. In addition, by including different noise levels within a defined range in the training process, the network can generalize the experimental data input and estimate the positioning of defects with similar quality. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000701701 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652022000701701 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0001-3765202220211577 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
publisher.none.fl_str_mv |
Academia Brasileira de Ciências |
dc.source.none.fl_str_mv |
Anais da Academia Brasileira de Ciências v.94 suppl.3 2022 reponame:Anais da Academia Brasileira de Ciências (Online) instname:Academia Brasileira de Ciências (ABC) instacron:ABC |
instname_str |
Academia Brasileira de Ciências (ABC) |
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ABC |
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ABC |
reponame_str |
Anais da Academia Brasileira de Ciências (Online) |
collection |
Anais da Academia Brasileira de Ciências (Online) |
repository.name.fl_str_mv |
Anais da Academia Brasileira de Ciências (Online) - Academia Brasileira de Ciências (ABC) |
repository.mail.fl_str_mv |
||aabc@abc.org.br |
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1754302872876482560 |