LAPSE:2021.0465
Published Article
LAPSE:2021.0465
Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence
Konstantinos Paraskevoudis, Panagiotis Karayannis, Elias P. Koumoulos
May 27, 2021
This work describes a novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision. Specifically, Neural Networks are developed for identifying 3D printing defects during the printing process by analyzing video captured from the process. Defects are likely to occur in 3D printed objects during the printing process, with one of them being stringing; they are mostly correlated to one of the printing parameters or the object’s geometries. The defect stringing can be on a large scale and is usually located in visible parts of the object by a capturing camera. In this case, an AI model (Deep Convolutional Neural Network) was trained on images where the stringing issue is clearly displayed and deployed in a live environment to make detections and predictions on a video camera feed. In this work, we present a methodology for developing and deploying deep neural networks for the recognition of stringing. The trained model can be successfully deployed (with appropriate assembly of required hardware such as microprocessors and a camera) on a live environment. Stringing can be then recognized in line with fast speed and classification accuracy. Furthermore, this approach can be further developed in order to make adjustments to the printing process. Via this, the proposed approach can either terminate the printing process or correct parameters which are related to the identified defect.
Keywords
3D printing, additive manufacturing, Artificial Intelligence, computer vision, neural network
Suggested Citation
Paraskevoudis K, Karayannis P, Koumoulos EP. Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence. (2021). LAPSE:2021.0465
Author Affiliations
Paraskevoudis K: IRES—Innovation in Research & Engineering Solutions, Rue Koningin Astritlaan 59B, 1780 Wemmel, Belgium
Karayannis P: IRES—Innovation in Research & Engineering Solutions, Rue Koningin Astritlaan 59B, 1780 Wemmel, Belgium
Koumoulos EP: IRES—Innovation in Research & Engineering Solutions, Rue Koningin Astritlaan 59B, 1780 Wemmel, Belgium [ORCID]
Journal Name
Processes
Volume
8
Issue
11
Article Number
E1464
Year
2020
Publication Date
2020-11-16
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr8111464, Publication Type: Journal Article
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LAPSE:2021.0465
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doi:10.3390/pr8111464
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May 27, 2021
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CC BY 4.0
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[v1] (Original Submission)
May 27, 2021
 
Verified by curator on
May 27, 2021
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http://psecommunity.org/LAPSE:2021.0465
 
Original Submitter
Calvin Tsay
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