Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces

[EN] Quality standards fulfilment is an essential task in manufacturing processes that involves high costs. One target is to avoid the presence of burrs in the edge of machine workpieces, which reduce the quality of the products. Furthermore, they are not easily removed since the part can even be da...

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Detalles Bibliográficos
Autores: Riego Del Castillo, Virginia, Sánchez González, Lidia, Strisciuglio, Nicola
Tipo de recurso: artículo
Estado:Versión aceptada para publicación
Fecha de publicación:2024
País:España
Institución:Ajuntament de Barcelona
Repositorio:BULERIA. Repositorio Institucional de la Universidad de León
OAI Identifier:oai:buleria.unileon.es:10612/19198
Acceso en línea:https://hdl.handle.net/10612/19198
Access Level:acceso abierto
Palabra clave:Ingenierías
Quality estimation
Milling machined parts
Burrs in workpiece
Convolutional Neural Network,
Computational Performance
CNN explanation
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spelling Optimized Network for Detecting Burr-Breakage in Images of Milling WorkpiecesRiego Del Castillo, VirginiaSánchez González, LidiaStrisciuglio, NicolaIngenieríasQuality estimationMilling machined partsBurrs in workpieceConvolutional Neural Network,Computational PerformanceCNN explanation[EN] Quality standards fulfilment is an essential task in manufacturing processes that involves high costs. One target is to avoid the presence of burrs in the edge of machine workpieces, which reduce the quality of the products. Furthermore, they are not easily removed since the part can even be damaged. In this paper, we propose an optimized Convolutional Neural Network, to detect the presence of burrs in images of milling parts. Its design is focused on the optimization of classification (accuracy) and performance metrics (training time and number of trainable parameters). The proposed architecture identifies burrs with a 91.16\% accuracy in the test set, outperforming existing models as EfficientNetB0. It also reduces the number of trainable parameters from other models as AlexNet by 1.5 million. The prediction process just takes 48.39 milliseconds per image. Finally, in order to check if the model gets a high activation in the region of interest, a visual explanation of the model is also carried out by using Gradient-weighted Class Activation Mapping.SIOxford University PressArquitectura y Tecnologia de ComputadoresEscuela de Ingenierias Industrial, Informática y Aeroespacial2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionhttps://hdl.handle.net/10612/19198reponame:BULERIA. Repositorio Institucional de la Universidad de Leóninstname:Ajuntament de BarcelonaInglésinfo:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108277GB-C21info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2021-126592OB-C21info:eu-repo/semantics/openAccessoai:buleria.unileon.es:10612/191982026-06-24T12:43:27Z
dc.title.none.fl_str_mv Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
title Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
spellingShingle Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
Riego Del Castillo, Virginia
Ingenierías
Quality estimation
Milling machined parts
Burrs in workpiece
Convolutional Neural Network,
Computational Performance
CNN explanation
title_short Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
title_full Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
title_fullStr Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
title_full_unstemmed Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
title_sort Optimized Network for Detecting Burr-Breakage in Images of Milling Workpieces
dc.creator.none.fl_str_mv Riego Del Castillo, Virginia
Sánchez González, Lidia
Strisciuglio, Nicola
author Riego Del Castillo, Virginia
author_facet Riego Del Castillo, Virginia
Sánchez González, Lidia
Strisciuglio, Nicola
author_role author
author2 Sánchez González, Lidia
Strisciuglio, Nicola
author2_role author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnologia de Computadores
Escuela de Ingenierias Industrial, Informática y Aeroespacial
dc.subject.none.fl_str_mv Ingenierías
Quality estimation
Milling machined parts
Burrs in workpiece
Convolutional Neural Network,
Computational Performance
CNN explanation
topic Ingenierías
Quality estimation
Milling machined parts
Burrs in workpiece
Convolutional Neural Network,
Computational Performance
CNN explanation
description [EN] Quality standards fulfilment is an essential task in manufacturing processes that involves high costs. One target is to avoid the presence of burrs in the edge of machine workpieces, which reduce the quality of the products. Furthermore, they are not easily removed since the part can even be damaged. In this paper, we propose an optimized Convolutional Neural Network, to detect the presence of burrs in images of milling parts. Its design is focused on the optimization of classification (accuracy) and performance metrics (training time and number of trainable parameters). The proposed architecture identifies burrs with a 91.16\% accuracy in the test set, outperforming existing models as EfficientNetB0. It also reduces the number of trainable parameters from other models as AlexNet by 1.5 million. The prediction process just takes 48.39 milliseconds per image. Finally, in order to check if the model gets a high activation in the region of interest, a visual explanation of the model is also carried out by using Gradient-weighted Class Activation Mapping.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/10612/19198
url https://hdl.handle.net/10612/19198
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-108277GB-C21
info:eu-repo/grantAgreement/AEI/Programa Estatal para Impulsar la Investigación Científico-Técnica y su Transferencia/PID2021-126592OB-C21
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Oxford University Press
publisher.none.fl_str_mv Oxford University Press
dc.source.none.fl_str_mv reponame:BULERIA. Repositorio Institucional de la Universidad de León
instname:Ajuntament de Barcelona
instname_str Ajuntament de Barcelona
reponame_str BULERIA. Repositorio Institucional de la Universidad de León
collection BULERIA. Repositorio Institucional de la Universidad de León
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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