1. Repositório Aberto da Universidade do Porto
  2. FEUP - Faculdade de Engenharia
  3. FEUP - Dissertação
Please use this identifier to cite or link to this item: https://hdl.handle.net/10216/153034
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dc.creatorJoão Gabriel Luís Patrício
dc.date.accessioned2026-09-23T01:31:53Z-
dc.date.available2026-09-23T01:31:53Z-
dc.date.issued2023-09-22
dc.date.submitted2023-09-27
dc.identifier.othersigarra:644555
dc.identifier.urihttps://hdl.handle.net/10216/153034-
dc.descriptionThe 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.
dc.description.abstractThe 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.
dc.language.isoeng
dc.rightsopenAccess
dc.subjectOutras ciências da engenharia e tecnologias
dc.subjectOther engineering and technologies
dc.titleAnomaly Detection on Multivariate Time-Series from Lithography Equipment using Machine Learning
dc.typeDissertação
dc.contributor.uportoFaculdade de Engenharia
dc.identifier.doi10.34626/2c4e-7m20
dc.identifier.tid203424476
dc.subject.fosCiências da engenharia e tecnologias::Outras ciências da engenharia e tecnologias
dc.subject.fosEngineering and technology::Other engineering and technologies
thesis.degree.disciplineMestrado em Engenharia e Ciência de Dados
thesis.degree.grantorFaculdade de Engenharia
thesis.degree.grantorUniversidade do Porto
thesis.degree.level1
rcaap.embargofctTrabalho efetuado em parceria com a empresa Colep, onde está descrita informação sensível sobre o processo de fabricação.
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