3D Printing Errors Detection During the Process
Keywords:
3D printing, errors, detection, machine learning
Abstract
Automated error detection in 3D printing is an important challenge that impacts not only the quality of the final parts but also operational efficiency, helping to minimize wasted time and material. Certain types of errors can even result in printer malfunctions. A widely used solution for monitoring the printing process involves employing a webcam to observe the process in real time, either alerting the operator or halting the print if an issue is detected. In this paper, a computer vision algorithm able to detect specific errors is proposed.
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References
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[3]. Tlegenov Y., Lu W. F., Hong, G. S., A dynamic model for current-based nozzle condition monitoring in fused deposition modelling, Prog Addit Manuf, 4, p. 211-223, 2019.
[4]. Spoerk M., et al., Effect of the printing bed temperature on the adhesion of parts produced by fused filament fabrication, Plastics, Rubber and Composites, 47:1, p.17-24, 2018.
[5]. Bakarzhiev V., et al., Research into 3d printing layer adhesion in ABS materials, Environment. Technologies. Resources, Proceedings of the International Scientific and Practical Conference, 3, p. 41-45, 2023.
[6]. Guidetti X., et al., Force controlled printing for material extrusion additive manufacturing, Additive Manufacturing, 89, 104297, p. 1-23, 2024.
[7]. Iftekar S. F., et al., Advancements and Limitations in 3D Printing Materials and Technologies: A Critical Review, Polymers, 15, 2519, p. 1-23, 2023.
[8]. Li L., McGuan R., Isaac R., Kavehpour P., Candler R., Improving precision of material extrusion 3D printing by in-situ monitoring & predicting 3D geometric deviation using conditional adversarial networks, Additive Manufacturing, 38, 101695, p. 1-12, 2021.
[9]. Isiani A., et al., Fault Detection in 3D Printing: A Study on Sensor Positioning and Vibrational Patterns, Sensors, 23(17), 7524, p. 1-29, 2023.
[10]. Scipioni S. I., Lambiase F., Error introduced by direct 3D printing of compression samples of PLA made by FDM process, Int J Adv Manuf Technol, 129, p. 4355-4368, 2023.
[11]. Holzmond O., Li X., In situ real time defect detection of 3D printed parts, Additive Manufacturing, 17, p. 135-142, 2017.
[12]. Ganitano G. S., Wallace S. G., Maruyama B., A hybrid metaheuristic and computer vision approach to closed-loop calibration of fused deposition modeling 3D printers, Prog Addit Manuf, 9, p. 767-777, 2024.
[13]. Wang Y., et al., Image Source Identification Using Convolutional Neural Networks in IoT Environment, Wireless Communications and Mobile Computing, p. 1-12, 2021.
[14]. Heriyadi P., et al., Image Processing in 3D Printing Application: Study Case of Liver Organ, International Seminar on Application for Technology of Information and Communication (iSemantic), Semarang, Indonesia, p. 372-376, 2022.
[15]. Straub J., Initial work on the characterization of additive manufacturing (3D printing) using software image analysis, Machines, 3(2), p. 55-71, 2015.
[16]. Okamoto T., Ura S., Verifying the Accuracy of 3D-Printed Objects Using an Image Processing System, J. Manuf. Mater. Process, 8, 94, p.1-21, 2024.
[17]. Ugandhar D., Shing C., Automated Process Monitoring in 3D Printing Using Supervised Machine Learning, Procedia Manufacturing, 26, p. 865-870, 2018.
[18]. Xijun Z., et al., Machine learning-driven 3D printing: A review, Applied Materials Today, 39, p. 1-20, 2024.
[19]. Songyuan G., et al., Research status and prospect of machine learning in construction 3D printing, Case Studies in Construction Materials, 18, p. 1-19, 2023.
[20]. Yakubu A., et al., Optimization of 4D/3D printing via machine learning: A systematic review, Hybrid Advances, 6, p. 1-21, 2024.
[21]. Omairi A., Ismail Z. H., Towards Machine Learning for Error Compensation in Additive Manufacturing, Applied Sciences, 11(5), 2375, p. 1-27, 2021.
[22]. Goh G., Applications of machine learning in 3D printing, Materials Today: Proceedings, 70, p. 95-100, 2022.
[23]. Kumar K. K., et al., Fault detection on the 3-D printed objective surface by using the SVM algorithm, Materials Today: Proceedings, p. 1-6, 2023.
[24]. Alafaghani A., et al., Experimental Optimization of Fused Deposition Modelling Processing Parameters: A Design-for-Manufacturing Approach, Procedia Manufacturing, 10, p.791-803, 2017.
[25]. Valizadeh M., Wolff S. J., Convolutional Neural Network applications in additive manufacturing: A review, Advances in Industrial and Manufacturing Engineering, 4, 100072, p. 1-12, 2022.
[26]. Kaisar K., et al., Convolutional Neural Network-Based Defect Detection Technique in FDM Technology, Procedia Computer Science, 231, p. 119-128, 2024.
[27]. Garland A., et al., Deep Convolutional Neural Networks as a Rapid Screening Tool for Complex Additively Manufactured Structures, Additive Manufacturing, 35, 101217, p. 1-12, 2020.
[28]. Rachmawati S., et al., Fine-Tuned CNN with Data Augmentation for 3D Printer Fault Detection, 13th International Conference on Information and Communication Technology Convergence (ICTC), p. 902-905, 2022.
[29]. ***, https://octoprint.org/download/.
[30]. ***, https://www.obico.io/the-spaghetti-detective.html.
[31]. ***, Petsiuk A. L., Pearce J. M., Open source computer vision-based layer-wise 3D printing analysis, Additive Manufacturing, 36, 101473, p. 1-17, 2020.
Published
2024-06-15
How to Cite
1.
MARIN F-B, MARIN M. 3D Printing Errors Detection During the Process. The Annals of “Dunarea de Jos” University of Galati. Fascicle IX, Metallurgy and Materials Science [Internet]. 15Jun.2024 [cited 14Mar.2025];47(2):18-2. Available from: https://gup.ugal.ro/ugaljournals/index.php/mms/article/view/7166
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