A cyber process control system based on pattern recognition and cloud computing
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Gestão & Produção |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100222 |
Resumo: | Abstract: This paper presents a novel simulation model of the Cyber Process Control System (CPCS) by combining pattern recognition and Cloud Computing (CC). This paper's originality arises from its aim to build a cloud computing platform for autonomous machines, and the exploration of manufacturing data to generate interpretable patterns to be used in process control decision making. The combining of Cloud technology and machine learning brings production to Industry 4.0. The proposed system is tested using data Carbon Fiber Reinforced Polymer (CFRP) routing process. The little information available about the manufacturing process of this type of material and the interaction between the production steps makes the manufacturing process quite difficult. This system generates interpretable rules of controllable operating parameters sent to the controller to keep the machining process within the limits of the specifications. The second step is activated during the drifting conditions in the machining step. Also, the simulation of the machining process is illustrated to generate the relations between input and output variables of the machining process. The findings of the corrective actions are illustrated and the interaction between the two industrial steps is simulated. Finally, current and future CPCS and CC applications in Industry 4.0 are discussed. |
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Gestão & Produção |
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A cyber process control system based on pattern recognition and cloud computingProcess controlPattern recognitionMulti-class logical analysis of dataCyber-physical systemIndustry 4.0Cloud computingAbstract: This paper presents a novel simulation model of the Cyber Process Control System (CPCS) by combining pattern recognition and Cloud Computing (CC). This paper's originality arises from its aim to build a cloud computing platform for autonomous machines, and the exploration of manufacturing data to generate interpretable patterns to be used in process control decision making. The combining of Cloud technology and machine learning brings production to Industry 4.0. The proposed system is tested using data Carbon Fiber Reinforced Polymer (CFRP) routing process. The little information available about the manufacturing process of this type of material and the interaction between the production steps makes the manufacturing process quite difficult. This system generates interpretable rules of controllable operating parameters sent to the controller to keep the machining process within the limits of the specifications. The second step is activated during the drifting conditions in the machining step. Also, the simulation of the machining process is illustrated to generate the relations between input and output variables of the machining process. The findings of the corrective actions are illustrated and the interaction between the two industrial steps is simulated. Finally, current and future CPCS and CC applications in Industry 4.0 are discussed.Universidade Federal de São Carlos2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100222Gestão & Produção v.29 2022reponame:Gestão & Produçãoinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCAR10.1590/1806-9649-2022v29e5221info:eu-repo/semantics/openAccessAli,Amr MohamedYacout,SoumayaRabeih,EladlShaban,Yassereng2022-07-29T00:00:00Zoai:scielo:S0104-530X2022000100222Revistahttps://www.gestaoeproducao.com/PUBhttps://old.scielo.br/oai/scielo-oai.phpgp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br1806-96490104-530Xopendoar:2022-07-29T00:00Gestão & Produção - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
A cyber process control system based on pattern recognition and cloud computing |
title |
A cyber process control system based on pattern recognition and cloud computing |
spellingShingle |
A cyber process control system based on pattern recognition and cloud computing Ali,Amr Mohamed Process control Pattern recognition Multi-class logical analysis of data Cyber-physical system Industry 4.0 Cloud computing |
title_short |
A cyber process control system based on pattern recognition and cloud computing |
title_full |
A cyber process control system based on pattern recognition and cloud computing |
title_fullStr |
A cyber process control system based on pattern recognition and cloud computing |
title_full_unstemmed |
A cyber process control system based on pattern recognition and cloud computing |
title_sort |
A cyber process control system based on pattern recognition and cloud computing |
author |
Ali,Amr Mohamed |
author_facet |
Ali,Amr Mohamed Yacout,Soumaya Rabeih,Eladl Shaban,Yasser |
author_role |
author |
author2 |
Yacout,Soumaya Rabeih,Eladl Shaban,Yasser |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Ali,Amr Mohamed Yacout,Soumaya Rabeih,Eladl Shaban,Yasser |
dc.subject.por.fl_str_mv |
Process control Pattern recognition Multi-class logical analysis of data Cyber-physical system Industry 4.0 Cloud computing |
topic |
Process control Pattern recognition Multi-class logical analysis of data Cyber-physical system Industry 4.0 Cloud computing |
description |
Abstract: This paper presents a novel simulation model of the Cyber Process Control System (CPCS) by combining pattern recognition and Cloud Computing (CC). This paper's originality arises from its aim to build a cloud computing platform for autonomous machines, and the exploration of manufacturing data to generate interpretable patterns to be used in process control decision making. The combining of Cloud technology and machine learning brings production to Industry 4.0. The proposed system is tested using data Carbon Fiber Reinforced Polymer (CFRP) routing process. The little information available about the manufacturing process of this type of material and the interaction between the production steps makes the manufacturing process quite difficult. This system generates interpretable rules of controllable operating parameters sent to the controller to keep the machining process within the limits of the specifications. The second step is activated during the drifting conditions in the machining step. Also, the simulation of the machining process is illustrated to generate the relations between input and output variables of the machining process. The findings of the corrective actions are illustrated and the interaction between the two industrial steps is simulated. Finally, current and future CPCS and CC applications in Industry 4.0 are discussed. |
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=S0104-530X2022000100222 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0104-530X2022000100222 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1806-9649-2022v29e5221 |
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 |
Universidade Federal de São Carlos |
publisher.none.fl_str_mv |
Universidade Federal de São Carlos |
dc.source.none.fl_str_mv |
Gestão & Produção v.29 2022 reponame:Gestão & Produção instname:Universidade Federal de São Carlos (UFSCAR) instacron:UFSCAR |
instname_str |
Universidade Federal de São Carlos (UFSCAR) |
instacron_str |
UFSCAR |
institution |
UFSCAR |
reponame_str |
Gestão & Produção |
collection |
Gestão & Produção |
repository.name.fl_str_mv |
Gestão & Produção - Universidade Federal de São Carlos (UFSCAR) |
repository.mail.fl_str_mv |
gp@dep.ufscar.br||revistagestaoemanalise@unichristus.edu.br |
_version_ |
1750118208150437888 |