Methodology for automatic process of the fired ceramic tile's internal defect using IR images and artificial neural network
Autor(a) principal: | |
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Data de Publicação: | 2011 |
Outros Autores: | |
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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Journal of the Brazilian Society of Mechanical Sciences and Engineering (Online) |
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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 |
_version_ |
1754734681870303232 |