Machine learning applied to an intelligent and adaptive robotic inspection station
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10198/19380 |
Resumo: | Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays. |
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Machine learning applied to an intelligent and adaptive robotic inspection stationMachine learningArtificial visionQuality controlIndustry 4.0Tensor-FlowConvolution neural networkDomínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e TecnologiasIndustry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays.Indústria 4.0 promove o uso de tecnologias emergentes, como Internet of Things (IoT), Big Data, artificial intelligence (AI) e cloud computing, sustentadas por sistemas ciberfísicos, como o designio de alcançar o que chamam de fábricas inteligentes. A ideia é descentralizar os sistemas de produção e permitir que a monotorização, a adaptação e a otimização sejam feitos em tempo real, com base na grande quantidade de dados disponíveis no ambiente fabril que alimentam o uso de técnicas de machine learning (ML). Esta revolução tecnológica trará ganhos significativos de produtividade, economia de recursos e custos de manutenção mais reduzidos, pois as máquinas terão informações para operar com mais eficiência, adaptáveis e acompanhar as flutuações de procura. Esta tese discute a aplicação de técnicas supervisionadas de ML, aliadas à visão artificial, para a implementação de uma estação de inspeção robótica inteligente, colaborativa e adaptativa, que realiza o controlo de qualidade de consolas HMI, equipados com botões de pressão e displays LCD. Técnicas de ML foram aplicadas para o reconhecimento facial do operador, para classificação do tipo de console HMI a ser inspecionado, para classificar a condição do estado dos botões de pressão e deteção de anomalias nos displays LCD. A solução desenvolvida alcança resultados promissores, com quase 100 % de precisão na correta classificação das consolas e anomalias nos botões de pressão, e também valores elevados de acerto na deteção de defeitos nos displays LCD.Leitão, PauloRodrigues, Pedro JoãoBiblioteca Digital do IPBVariz, Luís Sousa Pinto2019-06-27T16:27:28Z201920172019-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10198/19380TID:202258025enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-21T10:44:11Zoai:bibliotecadigital.ipb.pt:10198/19380Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T23:09:53.252697Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Machine learning applied to an intelligent and adaptive robotic inspection station |
title |
Machine learning applied to an intelligent and adaptive robotic inspection station |
spellingShingle |
Machine learning applied to an intelligent and adaptive robotic inspection station Variz, Luís Sousa Pinto Machine learning Artificial vision Quality control Industry 4.0 Tensor-Flow Convolution neural network Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
title_short |
Machine learning applied to an intelligent and adaptive robotic inspection station |
title_full |
Machine learning applied to an intelligent and adaptive robotic inspection station |
title_fullStr |
Machine learning applied to an intelligent and adaptive robotic inspection station |
title_full_unstemmed |
Machine learning applied to an intelligent and adaptive robotic inspection station |
title_sort |
Machine learning applied to an intelligent and adaptive robotic inspection station |
author |
Variz, Luís Sousa Pinto |
author_facet |
Variz, Luís Sousa Pinto |
author_role |
author |
dc.contributor.none.fl_str_mv |
Leitão, Paulo Rodrigues, Pedro João Biblioteca Digital do IPB |
dc.contributor.author.fl_str_mv |
Variz, Luís Sousa Pinto |
dc.subject.por.fl_str_mv |
Machine learning Artificial vision Quality control Industry 4.0 Tensor-Flow Convolution neural network Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
topic |
Machine learning Artificial vision Quality control Industry 4.0 Tensor-Flow Convolution neural network Domínio/Área Científica::Engenharia e Tecnologia::Outras Engenharias e Tecnologias |
description |
Industry 4.0 promotes the use of emergent technologies, such as Internet of Things (IoT), Big Data, Artificial Intelligence (AI) and cloud computing, sustained by cyber-physical systems to reach smart factories. The idea is to decentralize the production systems and allow to reach monitoring, adaptation and optimization to be made in real time, based on the large amount of data available at shop floor that feed the use of machine learning techniques. This technological revolution will bring significant productivity gains, resources savings and reduced maintenance costs, as machines will have information to operate more efficiently, adaptable and following demand fluctuations. This thesis discusses the application of supervised Machine Learning (ML) techniques allied with artificial vision, to implement an intelligent, collaborative and adaptive robotic inspection station, which carries out the Quality Control (QC) of Human Machine Interface (HMI) consoles, equipped with pressure buttons and Liquid Crystal Display (LCD) displays. Machine learning techniques were applied for the recognition of the operator’s face, to classify the type of HMI console to be inspected, to classify the state condition of the pressure buttons and detect anomalies in the LCD displays. The developed solution reaches promising results, with almost 100% accuracy in the correct classification of the consoles and anomalies in the pressure buttons, and also high values in the detection of defects in the LCD displays. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017 2019-06-27T16:27:28Z 2019 2019-01-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10198/19380 TID:202258025 |
url |
http://hdl.handle.net/10198/19380 |
identifier_str_mv |
TID:202258025 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799135363111845888 |