Geometric Distortion Detection in Fused Filament Fabrication Using Augmented Reality and Computer Vision-Reference-Cited by-同舟云学术

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Geometric Distortion Detection in Fused Filament Fabrication Using Augmented Reality and Computer Vision

Published:2023-08-09 Issue: Volume: Page:
ISSN:
Container-title:2023 3rd International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS)
language:
Short-container-title:

Author:

Wu George¹,Cheng Chi-Tsun²,Pang Toh Yen³,Miletic Miro⁴

Affiliation:

1. RMIT University,Electrical and Electronic,Melbourne,Australia

2. RMIT University,Manufacturing Materials and Mechatronics,Melbourne,Australia

3. RMIT University,Electrical and Biomedical,Melbourne,Australia

4. Aerospace MEMKO Pty. Ltd,Melbourne,Australia

Funder

CSIRO Australia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10335169/10335094/10335364.pdf?arnumber=10335364

Reference17 articles.

1. Autonomous Structural Visual Inspection Using Region-Based Deep Learning for Detecting Multiple Damage Types

2. Automatic Traffic Signs and Panels Inspection System Using Computer Vision

3. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques

4. Factors effecting real-time optical monitoring of fused filament 3D printing

5. Augmented vision and interactive monitoring in 3D printing process

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