Abstract
3D printing technology is a kind of rapid prototyping technology. In the 3D printing process, several common faults often happen, resulting in interruption of the printing process or poor-quality of the printed product. In order to maintain normal function of the 3D printer, users need to manually check the scene all the time. In order to perform real-time detection of faults in the printing process of the 3D printer, multiple sets of experiments were conducted. We use sensors to obtain multiple parameters of the 3D printer. The machine learning method is used to classify and detect whether the printing process is in a fault state. This method can effectively detect the fault condition that occurs during real-time 3D printing process and can be promoted in more 3D printers.
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References
E.L. Nyberg, A.L. Farris, B.P. Hung et al., 3D-printing technologies for craniofacial re-habilitation, reconstruction, and regeneration. Ann. Biomed. Eng. 45(1), 45 (2017)
C.W. Foster, M.P. Down, Y. Zhang et al., 3D printed graphene based energy storage devices. Sci. Rep. 7, 42233 (2017)
N. Bhattacharjee, A. Urrios, S. Kang et al., The upcoming 3D-printing revolution in microfluidics. Lab Chip 16(10), 1720 (2016)
J. Straub, Automated testing and quality assurance of 3D printing/3D printed hardware: Assessment for quality assurance and cybersecurity purposes. IEEE Autotestcon, pp. 1–5. IEEE, (2016)
W. Ruinan, W. Yao, Design of an intelligent monitoring system for the fluid-feeding system of the car assembly line. Electrical Automation (2017)
M. Ataş, Y. Yardimci, A. Temizel, A new approach to aflatoxin detection in chili pepper by machine vision. Comput. Electron. Agric. 87(9), 129–141 (2012)
X. Liu, Research on Establishment of Internet-based Product Quality Tracking System in Mobile Internet Age. Standard Science (2016)
G. Galdos, P. Tamigniaux, J.P. Morel et al. Vibration Sensor (2017)
M. Li, L. Zhao, The classification of human lower limb motion based on acceleration sensor, in Guidance, Navigation and Control Conference (IEEE, 2017), pp. 2210–2214
L. Guerriero, G. Guerriero, G. Grelle et al., Brief communication: a low-cost Ar-duino®-based wire extensometer for earth flow monitoring. Nat. Hazards Earth Syst. Sci. 17(6), 881–885 (2017)
A. Samourkasidis, I. Athanasiadis, A miniature data repository on a raspberry Pi. Electronics 6(1), 1 (2017)
R. Devore, G. Kerkyacharian, D. Picard et al., Mathematical methods for supervised learn-ing. Found. Comput. Math, 6(1), 3–58 (2017)
S. Edition, Applied logistic regression analysis. Technometrics 38(2), 184–186 (2017)
S.J. Rigatti, Random Forest. J. Insur. Med. 47, 31–39 (2017)
R. Fu, B. Li, Y. Gao et al., Content-based image retrieval based on CNN and SVM, in IEEE International Conference on Computer and Communications (IEEE, 2017), pp. 638–642
L. Zhang, J. Lin, R. Karim, Sliding window-based fault detection from high-dimensional data streams. IEEE Trans. Syst. Man Cybern. Syst. 47(2), 289–303 (2017)
N.T. Khajavi, A. Kuh, The goodness of covariance selection problem from AUC bounds. Communication, Control, and Computing (IEEE, 2017), pp. 1252–1258
J. Mai, L. Zhang, F. Tao et al., Customized production based on distributed 3D printing services in cloud manufacturing. Int. J. Adv. Manuf. Technol. 84(1–4), 71–83 (2016)
J. Mai, L. Zhang, F. Tao et al., Architecture of hybrid cloud for manufacturing enterprise. System Simulation and Scientific Computing (Springer Berlin, Heidelberg, 2012), pp. 365–372
Acknowledgements
The research is supported by the National High-Tech Research and Development Plan of China under Grant No. 2015AA042101.
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Li, B., Zhang, L., Ren, L., Luo, X. (2019). 3D Printing Fault Detection Based on Process Data. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 529. Springer, Singapore. https://doi.org/10.1007/978-981-13-2291-4_38
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DOI: https://doi.org/10.1007/978-981-13-2291-4_38
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