Real-Time Vision-Based Fault Detection System for FDM 3D Printing Using Convolutional Neural Networks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00378534" target="_blank" >RIV/68407700:21220/24:00378534 - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1109/ME61309.2024.10789707" target="_blank" >http://dx.doi.org/10.1109/ME61309.2024.10789707</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ME61309.2024.10789707" target="_blank" >10.1109/ME61309.2024.10789707</a>

Result language
angličtina
Original language name
Real-Time Vision-Based Fault Detection System for FDM 3D Printing Using Convolutional Neural Networks
Original language description
We present a real-time vision-based fault detection system for Fused Deposition Modeling (FDM) 3D printing, utilizing multiple You Only Look Once (YOLO) Convolutional Neural Network (CNN) models—YOLOv4 Tiny, YOLOv5, YOLOv8, and YOLOv10—to identify defects such as blobs, cracks, spaghetti, stringing, under-extrusion, and warping. Unlike previous studies that use single models, our system integrates these YOLO variants to enhance detection accuracy and incorporates an adaptive learning module for continuous improvement based on real-time data. The system logs and displays faults on a live camera feed, significantly improving quality control in additive manufacturing. Comparative analysis shows that YOLOv8 achieves a 7% increase in detection accuracy and a 30% reduction in print errors. This novel integration of multiple YOLO models with adaptive learning advances automation and reliability in 3D printing.
Czech name
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Czech description
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Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
2024 21st International Conference on Mechatronics - Mechatronika (ME)
ISBN
979-8-3503-9490-0
ISSN
—
e-ISSN
—
Number of pages
9
Pages from-to
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Publisher name
IEEE
Place of publication
New Jersey
Event location
Brno
Event date
Dec 4, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001414274500021

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