Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning

Detalhes bibliográficos
Autor(a) principal: João Gabriel Luís Patrício
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/10216/153034
Resumo: The present work is mainly motivated by the challenges embraced by the metallic packing industry, in its path along the fourth industrial revolution (Industry 4.0). This work serves to bring Artificial Intelligence (AI) to a mass production lithography process to detect anomalous patterns, using Outlier Detection (OD) algorithms to prevent non-conformities and support quality control operators. All the OD algorithms deployment is based on Machine Learning (ML) techniques, scratching the surface of the process and quality monitoring applications in industrial scenarios.
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spelling Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine LearningOutras ciências da engenharia e tecnologiasOther engineering and technologiesThe present work is mainly motivated by the challenges embraced by the metallic packing industry, in its path along the fourth industrial revolution (Industry 4.0). This work serves to bring Artificial Intelligence (AI) to a mass production lithography process to detect anomalous patterns, using Outlier Detection (OD) algorithms to prevent non-conformities and support quality control operators. All the OD algorithms deployment is based on Machine Learning (ML) techniques, scratching the surface of the process and quality monitoring applications in industrial scenarios.2023-09-222023-09-22T00:00:00Z2026-09-21T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/10216/153034TID:203424476engJoão Gabriel Luís Patrícioinfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-12-22T01:32:45Zoai:repositorio-aberto.up.pt:10216/153034Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T00:11:46.261453Repositório 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 Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
title Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
spellingShingle Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
João Gabriel Luís Patrício
Outras ciências da engenharia e tecnologias
Other engineering and technologies
title_short Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
title_full Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
title_fullStr Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
title_full_unstemmed Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
title_sort Anomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
author João Gabriel Luís Patrício
author_facet João Gabriel Luís Patrício
author_role author
dc.contributor.author.fl_str_mv João Gabriel Luís Patrício
dc.subject.por.fl_str_mv Outras ciências da engenharia e tecnologias
Other engineering and technologies
topic Outras ciências da engenharia e tecnologias
Other engineering and technologies
description The present work is mainly motivated by the challenges embraced by the metallic packing industry, in its path along the fourth industrial revolution (Industry 4.0). This work serves to bring Artificial Intelligence (AI) to a mass production lithography process to detect anomalous patterns, using Outlier Detection (OD) algorithms to prevent non-conformities and support quality control operators. All the OD algorithms deployment is based on Machine Learning (ML) techniques, scratching the surface of the process and quality monitoring applications in industrial scenarios.
publishDate 2023
dc.date.none.fl_str_mv 2023-09-22
2023-09-22T00:00:00Z
2026-09-21T00:00:00Z
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 https://hdl.handle.net/10216/153034
TID:203424476
url https://hdl.handle.net/10216/153034
identifier_str_mv TID:203424476
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
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
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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
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