A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Production |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100401 |
Resumo: | Abstract Paper aims This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations. Originality Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing. Research method In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose. |
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A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industryData miningDefectDetectionClassificationManufacturingAbstract Paper aims This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations. Originality Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing. Research method In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose.Associação Brasileira de Engenharia de Produção2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100401Production v.32 2022reponame:Productioninstname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPRO10.1590/0103-6513.20210097info:eu-repo/semantics/openAccessBártová,BlankaBína,VladislavVáchová,Lucieeng2022-01-20T00:00:00Zoai:scielo:S0103-65132022000100401Revistahttps://www.scielo.br/j/prod/https://old.scielo.br/oai/scielo-oai.php||production@editoracubo.com.br1980-54110103-6513opendoar:2022-01-20T00:00Production - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
title |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
spellingShingle |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry Bártová,Blanka Data mining Defect Detection Classification Manufacturing |
title_short |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
title_full |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
title_fullStr |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
title_full_unstemmed |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
title_sort |
A PRISMA-driven systematic review of data mining methods used for defects detection and classification in the manufacturing industry |
author |
Bártová,Blanka |
author_facet |
Bártová,Blanka Bína,Vladislav Váchová,Lucie |
author_role |
author |
author2 |
Bína,Vladislav Váchová,Lucie |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Bártová,Blanka Bína,Vladislav Váchová,Lucie |
dc.subject.por.fl_str_mv |
Data mining Defect Detection Classification Manufacturing |
topic |
Data mining Defect Detection Classification Manufacturing |
description |
Abstract Paper aims This research aims to analyze the primary studies published in recent years focusing on defect detection or classification in manufacturing and extract information about frequently used data mining (DM) methods, their accuracy, strengths, and limitations. Originality Industrial production is now undergoing a dynamic transformation in the context of Industry 4.0, where implementation of data mining is a frequently discussed topic, and such an overall summary is missing. Research method In this study, the PRISMA-driven systematic literature review is combined with the approach defined by Kitchenham (2004). Main findings The most frequently used data mining methods for defect detection are Bayesian network (BN) and Support vector machine (SVM). Besides previously mentioned methods, the Decision trees (DT) and Clustering are often used for defect classification. Neural Networks (NN) use is common for both defect detection and classification. DT, together with the Genetic algorithm (GA) and SVM, achieved the highest average accuracy. Recently, authors often combine different DM methods, and also methods for data dimensionality reduction are often used. Implications for theory and practice This study contributes to the quality management literature by extending a summary of recently used DM methods for defect detection and classification. This summary can help researchers choose a suitable method and build models for achieving its research purpose. |
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=S0103-65132022000100401 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0103-65132022000100401 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0103-6513.20210097 |
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 de Produção |
publisher.none.fl_str_mv |
Associação Brasileira de Engenharia de Produção |
dc.source.none.fl_str_mv |
Production v.32 2022 reponame:Production instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Production |
collection |
Production |
repository.name.fl_str_mv |
Production - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
||production@editoracubo.com.br |
_version_ |
1754213154884157440 |