[1] Tzampazaki M, Zografos C, Vrochidou E, Papakostas GA. Machine Vision—Moving from Industry 4.0 to Industry 5.0. Applied Sciences. 2024 Feb 11;14(4):1471.
doi: 10.3390/app14041471
[2] Scheffel RM, Fröhlich AA, Silvestri M. Automated fault detection for additive manufacturing using vibration sensors. International Journal of Computer Integrated Manufacturing. 2021 May 4;34(5):500–14.
doi: 10.1080/0951192X.2021.19013
[3] Lu L, Zheng J, Mishra S. A model-based layer-to-layer control algorithm for ink-jet 3d printing. InDynamic systems and control conference 2014 Oct 22 (Vol. 46193, p. V002T35A001). American Society of Mechanical Engineers.
doi: 10.1115/DSCC2014-5914
[4] Wu H, Wang Y, Yu Z. In situ monitoring of FDM machine condition via acoustic emission. International Journal of Advanced Manufacturing Technology. 2015 Sep 17.
doi: 10.1007/s00170-015-7809-4
[5] Zhang S, Sun Z, Long J, Li C, Bai Y. Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders. Computers in Industry. 2019 Feb 105:164–76.
doi: 10.1016/j.compind.2018.12.004
[6] Meng L, McWilliams B, Jarosinski W, Park HY, Jung YG, Lee J, et al. Machine Learning in Additive Manufacturing: A Review. Journal of The Minerals, Metals & Materials Society. 2020 Jun 1;72(6):2363–77.
doi: 10.1007/s11837-020-04155-y
[7] Nuchitprasitchai S, Roggemann M, Pearce J. Three Hundred and Sixty Degree Real-Time Monitoring of 3-D Printing Using Computer Analysis of Two Camera Views. Journal of Manufacturing and Materials Processing. 2017 Jul 4;1(1):2.
doi: 10.3390/jmmp1010002
[8] Fang T, Bakhadyrov I, Jafari MA, Alpan G. Online detection of defects in layered manufacturing. InProceedings. 1998 IEEE international conference on robotics and automation (Cat. No. 98CH36146) 1998 May 20 (Vol. 1, pp. 254-259). IEEE.
doi: 10.1109/ROBOT.1998.676386
[9] Liu C, Law ACC, Roberson D, Kong Z (James). Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication. Journal of Manufacturing Systems. 2019 Apr 51:75–86.
doi: 10.1016/j.jmsy.2019.04.002
[10] Wu H, Yu Z, Wang Y. A new approach for online monitoring of additive manufacturing based on acoustic emission. InInternational Manufacturing Science and Engineering Conference 2016 Jun 27 (Vol. 49910, p. V003T08A013). American Society of Mechanical Engineers.
doi: 10.1115/MSEC2016-8551
[11] Shevchik SA, Masinelli G, Kenel C, Leinenbach C, Wasmer K. Deep Learning for In Situ and Real-Time Quality Monitoring in Additive Manufacturing Using Acoustic Emission. Institute of Electrical and Electronics Engineers Transactions on Industrial Informatics. 2019 Sep 15(9):5194–203.
doi: 10.1109/TII.2019.2910524
[12] Liu C, Le Roux L, Ji Z, Kerfriden P, Lacan F, Bigot S. Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing. Procedia Computer Science. 2020 Jan 1;176:2586-95.
doi: 10.1016/j.procs.2020.09.314
[13] Li Y, Zhao W, Li Q, Wang T, Wang G. In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors. Sensors. 2019 Jun 6;19(11):2589.
doi: 10.3390/s19112589
[14] Wang Y, Huang J, Wang Y, Feng S, Peng T, Yang H, et al. A CNN-Based Adaptive Surface Monitoring System for Fused Deposition Modeling. Institute of Electrical and Electronics Engineers/American Society of Mechanical Engineers Transactions on Mechatronics. 2020 Oct 25(5):2287–96.
doi: 10.1109/TMECH.2020.2996223
[15] Karna NB, Putra MA, Rachmawati SM, Abisado M, Sampedro GA. Toward accurate fused deposition modeling 3d printer fault detection using improved YOLOv8 with hyperparameter optimization. IEEE Access. 2023 Jul 7;11:74251-62.
doi: 10.1109/ACCESS.2023.3293056
[16] Lu L, Hou J, Yuan S, Yao X, Li Y, Zhu J. Deep learning-assisted real-time defect detection and closed-loop adjustment for additive manufacturing of continuous fiber-reinforced polymer composites. Robotics and Computer-Integrated Manufacturing. 2023 Feb 1;79:102431.
doi: 10.1016/j.rcim.2022.102431
[17] Siahvashi A, Shahbazi M, Niknam SA. The use of a neuro-fuzzy network coupled with meta-heuristic learning methods to predict surface roughness in the machining of aluminum alloys. Iranian Journal of Manufacturing Engineering. 2024 Jan 21;10(11):49-60.
doi: 10.22034/IJME.2024.412021.1816 [In Persian]
[18] Mohammadi N, Nategh MJ. Development an advanced neural network for recognition of machining feature.Iranian Journal of Manufacturing Engineering. 2022 Jul 23;9(5):1-2.
doi: 10.22034/IJME.2022.160054[In Persian]
