Exploring Machine Learning-Based Fault Monitoring for Polymer-Based Additive Manufacturing: Challenges and Opportunities.

Sampedro, Gabriel Avelino R; Rachmawati, Syifa Maliah; Kim, Dong-Seong; Lee, Jae-Min

Sampedro, Gabriel Avelino R; Rachmawati, Syifa Maliah; Kim, Dong-Seong; Lee, Jae-Min.

Afiliação

Sampedro GAR; Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.
Rachmawati SM; College of Computer Studies, De La Salle University, 2401 Taft Ave., Malate, Manila 1004, Philippines.
Kim DS; Faculty of Information and Communication Studies, University of the Philippines Open University, Los Baños 4031, Philippines.
Lee JM; Department of IT Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Republic of Korea.

Sensors (Basel) ; 22(23)2022 Dec 02.

Article em En | MEDLINE | ID: mdl-36502146

Assuntos

Aprendizado de Máquina; Polímeros; Comércio; Algoritmos; Impressão Tridimensional

Palavras-chave

3D printing; machine learning; nozzle clogging; smart monitoring

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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Polímeros / Aprendizado de Máquina Idioma: En Revista: Sensors (Basel) Ano de publicação: 2022 Tipo de documento: Article

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