Detection and Classification of Defects in 3D Printing using a Novel Skewness and Kurtosis-based Parameter of Sound Signals and Machine Learning

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorMaterial and Industrial Production Engineering-
Autor(es): dc.creatorLopes, Thiago Glissoi-
Autor(es): dc.creatorKennerly, Victoria Dutra-
Autor(es): dc.creatorAguiar, Paulo Roberto-
Autor(es): dc.creatorJunior, Cristiano Soares-
Autor(es): dc.creatorDe Carvalho Monson, Paulo Monteiro-
Autor(es): dc.creatorDaddona, Doriana Marilena-
Data de aceite: dc.date.accessioned2025-08-21T15:31:52Z-
Data de disponibilização: dc.date.available2025-08-21T15:31:52Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-12-31-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1109/ICCAD60883.2024.10553900-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/306672-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/306672-
Descrição: dc.descriptionThis work proposes a monitoring strategy based on kurtosis and skewness of sound signals to detect and classify the machine conditions in fused deposition modeling (FDM). The methodology consisted in experimental tests conducted in a 3D printer in which an electret microphone was attached to the extruder support. The signals were acquired by an oscilloscope at 200 kHz, and then digitally processed in MATLAB. The results showed that the proposed parameter along with machine learning models produced a significant improvement when compared to the use of the skewness and kurtosis alone.-
Descrição: dc.descriptionSão Paulo State University - Unesp Department of Electrical Engineering-
Descrição: dc.descriptionUniversity of São Paulo - Usp Department of Electrical and Computer Engineering-
Descrição: dc.descriptionUniversity of Naples Frederico Ii - UniNa Department of Chemical Material and Industrial Production Engineering-
Descrição: dc.descriptionSão Paulo State University - Unesp Department of Electrical Engineering-
Idioma: dc.languageen-
Relação: dc.relation2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024-
???dc.source???: dc.sourceScopus-
Palavras-chave: dc.subject3D printing-
Palavras-chave: dc.subjectCondition Monitoring-
Palavras-chave: dc.subjectFused Deposition Modeling-
Palavras-chave: dc.subjectMachine learning-
Título: dc.titleDetection and Classification of Defects in 3D Printing using a Novel Skewness and Kurtosis-based Parameter of Sound Signals and Machine Learning-
Tipo de arquivo: dc.typeaula digital-
Aparece nas coleções:Repositório Institucional - Unesp

Não existem arquivos associados a este item.