Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network

Detalhes bibliográficos
Autor(a) principal: Andrade,Roberto Márcio de
Data de Publicação: 2011
Outros Autores: Eduardo,Alexandre Carlos
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782011000100010
Resumo: In the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), detecting defected or no defected tile. The system has been applied to detect on-line measurement results achieved at the exit of the press. The above automatic inspection procedures have been implemented and tested on a number of tiles using synthetic and real defects. The results obtained confirmed the efficiency of the methodology defect detection in raw tile and its relevance as a promising approach on-line, as well as included in quality control and inspection programs.
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spelling Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural networkceramic tilesdefect detectioninfrared imagesneural networkIn the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), detecting defected or no defected tile. The system has been applied to detect on-line measurement results achieved at the exit of the press. The above automatic inspection procedures have been implemented and tested on a number of tiles using synthetic and real defects. The results obtained confirmed the efficiency of the methodology defect detection in raw tile and its relevance as a promising approach on-line, as well as included in quality control and inspection programs.Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM2011-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782011000100010Journal of the Brazilian Society of Mechanical Sciences and Engineering v.33 n.1 2011reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)instacron:ABCM10.1590/S1678-58782011000100010info:eu-repo/semantics/openAccessAndrade,Roberto Márcio deEduardo,Alexandre Carloseng2011-05-02T00:00:00Zoai:scielo:S1678-58782011000100010Revistahttps://www.scielo.br/j/jbsmse/https://old.scielo.br/oai/scielo-oai.php||abcm@abcm.org.br1806-36911678-5878opendoar:2011-05-02T00:00Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)false
dc.title.none.fl_str_mv Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
title Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
spellingShingle Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
Andrade,Roberto Márcio de
ceramic tiles
defect detection
infrared images
neural network
title_short Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
title_full Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
title_fullStr Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
title_full_unstemmed Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
title_sort Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
author Andrade,Roberto Márcio de
author_facet Andrade,Roberto Márcio de
Eduardo,Alexandre Carlos
author_role author
author2 Eduardo,Alexandre Carlos
author2_role author
dc.contributor.author.fl_str_mv Andrade,Roberto Márcio de
Eduardo,Alexandre Carlos
dc.subject.por.fl_str_mv ceramic tiles
defect detection
infrared images
neural network
topic ceramic tiles
defect detection
infrared images
neural network
description In the ceramic industry, rarely testing systems were employed to on-line detect the presence of defects in ceramic tiles. This paper is concerned with the problem of automatic inspection of ceramic tiles using Infrared Images and Artificial Neural Network (ANN). The performance of the technique has been evaluated theoretically and experimentally from laboratory and on line tile samples. It has been performed system for IR image processing and, utilizing an Artificial Neural Network (ANN), detecting defected or no defected tile. The system has been applied to detect on-line measurement results achieved at the exit of the press. The above automatic inspection procedures have been implemented and tested on a number of tiles using synthetic and real defects. The results obtained confirmed the efficiency of the methodology defect detection in raw tile and its relevance as a promising approach on-line, as well as included in quality control and inspection programs.
publishDate 2011
dc.date.none.fl_str_mv 2011-03-01
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=S1678-58782011000100010
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1678-58782011000100010
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S1678-58782011000100010
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 Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
publisher.none.fl_str_mv Associação Brasileira de Engenharia e Ciências Mecânicas - ABCM
dc.source.none.fl_str_mv Journal of the Brazilian Society of Mechanical Sciences and Engineering v.33 n.1 2011
reponame:Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
instname:Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
instacron:ABCM
instname_str Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
instacron_str ABCM
institution ABCM
reponame_str Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
collection Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online)
repository.name.fl_str_mv Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) - Associação Brasileira de Engenharia e Ciências Mecânicas (ABCM)
repository.mail.fl_str_mv ||abcm@abcm.org.br
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