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Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence

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Processes 2020, 8(11), 1464; https://doi.org/10.3390/pr8111464
Received: 15 October 2020 / Revised: 8 November 2020 / Accepted: 12 November 2020 / Published: 16 November 2020
(This article belongs to the Section Process Control and Supervision)
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. View Full-Text
Keywords: 3D printing; additive manufacturing; artificial intelligence; computer vision; neural network 3D printing; additive manufacturing; artificial intelligence; computer vision; neural network
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MDPI and ACS Style

Paraskevoudis, K.; Karayannis, P.; Koumoulos, E.P. Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence. Processes 2020, 8, 1464. https://doi.org/10.3390/pr8111464

AMA Style

Paraskevoudis K, Karayannis P, Koumoulos EP. Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence. Processes. 2020; 8(11):1464. https://doi.org/10.3390/pr8111464

Chicago/Turabian Style

Paraskevoudis, Konstantinos, Panagiotis Karayannis, and Elias P. Koumoulos 2020. "Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence" Processes 8, no. 11: 1464. https://doi.org/10.3390/pr8111464

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