Detection and Classification of Defects in 3D Printing using a Novel Skewness and Kurtosis-based Parameter of Sound Signals and Machine Learning-Reference-Cited by-同舟云学术

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

Published:2024-05-15 Issue: Volume:69 Page:1-5
ISSN:
Container-title:2024 International Conference on Control, Automation and Diagnosis (ICCAD)
language:
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Author:

Lopes Thiago Glissoi¹,Kennerly Victoria Dutra¹,Aguiar Paulo Roberto¹,Junior Cristiano Soares¹,De Carvalho Monson Paulo Monteiro²,Daddona Doriana Marilena³

Affiliation:

1. São Paulo State University - UNESP,Department of Electrical Engineering,Bauru,Brazil

2. University of São Paulo - USP,Department of Electrical and Computer Engineering,São Carlos,Brazil

3. University of Naples Frederico II - UniNa,Department of Chemical, Material and Industrial Production Engineering,Naples,Italy

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10553373/10553653/10553900.pdf?arnumber=10553900

Reference14 articles.

1. Design and Manufacturing of a 3D printer filaments extruder

2. Gaining a better understanding of the extrusion process in fused filament fabrication 3D printing: a review

3. THE INFLUENCE OF PRINTING PARAMETERS ON MONOLAYER PART FEATURES IN FUSED FILAMENT FABRICATION

4. Fused Deposition Modeling 3D Printing Fault Diagnosis using Temporal Convolutional Network

5. A Study on Acoustic Signals from an Electret Microphone in 3D Printing (under publishing);Aguiar

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