Vol.13, No.2, May 2024.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333


TEM Journal



Association for Information Communication Technology Education and Science

Prediction of Errors in the Field of Additive Manufacturing Technology


Martin Pollák, Peter Gabštur, Marek Kočiško


© 2024 Martin Pollák, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)


Citation Information: TEM Journal. Volume 13, Issue 2, Pages 849-855, ISSN 2217-8309, DOI: 10.18421/TEM132-01, May 2024.


Received: 13 December 2023.

Revised:   19 February 2024.
Accepted: 06 March 2024.
Published: 28 May 2024.




Additive manufacturing, also known as 3D printing, allows the formation of complex geometric structures layer by layer. Predicting errors in this process may help identify potential problems in a timely manner and minimise waste. A human may detect an additive manufacturing error, but cannot provide continuous monitoring or real-time correction. The article is focused on the design of a camera system design for online monitoring of the 3D printing process with the task of detecting process errors arising during 3D printing of objects. The article describes the methodology for tracking the occurrence of process errors in 3D printing, which are identified in the OctoPrint Nexus AI plug-in environment for the subsequent application of a suitable solution to minimize the occurrence of defects. The application of a real-time process monitoring system including the ability to correctly predict anomalous behaviour in the context of artificial intelligence has proven to be an appropriate solution to that particular problem.


Keywords –Additive manufacturing, artificial intelligence, Raspberry pi, online control, process errors.



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