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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2024 21st International Conference on Mechatronics - Mechatronika (ME)

ISBN

979-8-3503-9490-0

ISSN

—

e-ISSN

—

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

New Jersey

Místo konání akce

Brno

Datum konání akce

4. 12. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001414274500021