Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 26, 2025

Fused Filament Fabrication FFF of Metal-Ceramic Components
08:43

Fused Filament Fabrication FFF of Metal-Ceramic Components

Published on: January 11, 2019

17.3K

Anomaly Detection in Fused Filament Fabrication Using Machine Learning.

Guo Dong Goh1, Nur Muizzu Bin Hamzah1, Wai Yee Yeong1,2,3

  • 1Singapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University Singapore, Singapore, Singapore.

3D Printing and Additive Manufacturing
|June 22, 2023
PubMed
Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Customizable Fabrication of 2D and Conformal Multielectrode Arrays for 3D Printed Organotypic Bioelectronic Interfaces.

Advanced healthcare materials·2025
Same author

Progress and Opportunities for Machine Learning in Materials and Processes of Additive Manufacturing.

Advanced materials (Deerfield Beach, Fla.)·2024
Same author

Automated Service Height Fault Detection Using Computer Vision and Machine Learning for Badminton Matches.

Sensors (Basel, Switzerland)·2023
Same author

Understanding droplet jetting on varying substrate for biological applications.

International journal of bioprinting·2023
Same author

3D printing and 3D-printed electronics: Applications and future trends in smart drug delivery devices.

International journal of bioprinting·2023
Same author

The Design and Development of Instrumented Toys for the Assessment of Infant Cognitive Flexibility.

Sensors (Basel, Switzerland)·2023

This study introduces an on-site monitoring system for fused filament fabrication (FFF) 3D printing, using computer vision to detect and correct defects in real-time. The system enhances part quality and print repeatability in additive manufacturing.

Area of Science:

  • Additive Manufacturing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Fused filament fabrication (FFF) is a widely adopted 3D printing technology with growing industrial use.
  • Key limitations include inconsistent part quality and print repeatability due to manufacturing-induced defects.
  • Existing FFF processes lack real-time defect detection and correction mechanisms.

Purpose of the Study:

  • To develop and implement an on-site monitoring system for extrusion-based 3D printers.
  • To integrate computer vision and object detection for real-time defect identification and correction.
  • To improve the quality and repeatability of FFF 3D printed parts.

Main Methods:

  • A monitoring system with a print-head-attached camera and a laptop for video feed processing was developed.
Keywords:
3D printingartificial intelligentfused deposition modelingin situ monitoringsupervised learning

More Related Videos

Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

1.3K
Production of a Strain-Measuring Device with an Improved 3D Printer
06:17

Production of a Strain-Measuring Device with an Improved 3D Printer

Published on: January 30, 2020

6.2K

Related Experiment Videos

Last Updated: Jul 26, 2025

Fused Filament Fabrication FFF of Metal-Ceramic Components
08:43

Fused Filament Fabrication FFF of Metal-Ceramic Components

Published on: January 11, 2019

17.3K
Additive Manufacturing-Enabled Low-Cost Particle Detector
06:05

Additive Manufacturing-Enabled Low-Cost Particle Detector

Published on: March 24, 2023

1.3K
Production of a Strain-Measuring Device with an Improved 3D Printer
06:17

Production of a Strain-Measuring Device with an Improved 3D Printer

Published on: January 30, 2020

6.2K
  • Various YOLO (You Only Look Once) architectures were evaluated for detecting under-extrusion and over-extrusion defects.
  • Optimized YOLO models (YOLOv3-Tiny) using ONNX for improved inference speed and implemented a G-code based correction algorithm.

    • Main Results:

    • YOLOv3 and YOLOv4 Tiny models achieved >80% mean average precision (AP50) in defect detection.
    • Optimized models achieved 89.8% classification accuracy and 70 frames per second inference speed.
    • Demonstrated successful real-time monitoring and autonomous correction of defects during the FFF process.

    Conclusions:

    • The developed on-site monitoring system effectively detects and corrects defects in FFF 3D printing in real-time.
    • This closed-loop feedback system significantly enhances part quality and print repeatability.
    • The approach provides a foundation for advanced monitoring and correction in various additive manufacturing processes.