Phát hiện các lỗi trong quá trình ép vật liệu thông qua học sâu
Tóm tắt
Từ khóa
Tài liệu tham khảo
Weller, 2015, Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited, Int. J. Prod. Econ., 164, 43, 10.1016/j.ijpe.2015.02.020
Redwood, B. (2020, May 06). Additive Manufacturing Technologies: An Overview. Available online: https://www.3dhubs.com/knowledge-base/additive-manufacturing-technologies-overview.
Dehghanghadikolaei, 2019, Improving corrosion resistance of additively manufactured nickel–titanium biomedical devices by micro-arc oxidation process, J. Mater. Sci., 54, 7333, 10.1007/s10853-019-03375-1
Gannarapu, 2019, Micro-extrusion-based additive manufacturing with liquid metals and alloys: Flow and deposition driven by oxide skin mechanics, Extreme Mech. Lett., 33, 100554, 10.1016/j.eml.2019.100554
Mehrpouya, M., Dehghanghadikolaei, A., Fotovvati, B., Vosooghnia, A., Emamian, S.S., and Gisario, A. (2019). The potential of additive manufacturing in the smart factory industrial 4.0: A review. Appl. Sci., 9.
Terry, S., Lu, H., Fidan, I., Zhang, Y., Tantawi, K., Guo, T., and Asiabanpour, B. (2020). The Influence of Smart Manufacturing towards Energy Conservation: A Review. Technologies, 8.
Fidan, 2019, The trends and challenges of fiber reinforced additive manufacturing, Int. J. Adv. Manuf. Technol., 102, 1801, 10.1007/s00170-018-03269-7
Mohri, M., Rostamizadeh, A., and Talwalkar, A. (2018). Foundations of Machine Learning, MIT Press.
Zhang, Z. (2019). Detection of the Additive Manufacturing In-Process Failures via Deep Learning. [Master’s Thesis, Tennessee Technological University].
Ching, 2018, Opportunities and obstacles for deep learning in biology and medicine, J. R. Soc. Interf., 15, 20170387, 10.1098/rsif.2017.0387
Najafabadi, 2015, Deep learning applications and challenges in big data analytics, J. Big Data, 2, 1, 10.1186/s40537-014-0007-7
Mamoshina, 2016, Applications of deep learning in biomedicine, Mol. Pharm., 13, 1445, 10.1021/acs.molpharmaceut.5b00982
Deng, 2014, Deep learning: Methods and applications, Found. Trends Signal Process., 7, 197, 10.1561/2000000039
Khanzadeh, 2018, Quantifying geometric accuracy with unsupervised machine learning: Using self-organizing map on fused filament fabrication additive manufacturing parts, J. Manuf. Sci. Eng., 140, 031011, 10.1115/1.4038598
Baumann, 2016, Vision based error detection for 3D printing processes, MATEC Web Conf., 591, 1
Delli, 2018, Automated process monitoring in 3D printing using supervised machine learning, Procedia Manuf., 26, 865, 10.1016/j.promfg.2018.07.111
Krizhevsky, 2012, Imagenet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 25, 1097
Liu, 2019, Image analysis-based closed loop quality control for additive manufacturing with fused filament fabrication, J. Manuf. Syst., 51, 75, 10.1016/j.jmsy.2019.04.002
Gardner, 2019, Machines as Craftsmen: Localized Parameter Setting Optimization for Fused Filament Fabrication 3D Printing, Adv. Mater. Technol., 4, 1800653, 10.1002/admt.201800653
Li, Y., Zhao, W., Li, Q., Wang, T., and Wang, G. (2019). In-Situ Monitoring and Diagnosing for Fused Filament Fabrication Process Based on Vibration Sensors. Sensors, 19.
Salmi, 2016, Effect of build orientation in 3D printing production for material extrusion, material jetting, binder jetting, sheet object lamination, vat photopolymerisation, and powder bed fusion, Int. J. Collab. Enterp., 5, 218
Turner, 2014, A review of melt extrusion additive manufacturing processes: I. Process design and modeling, Rapid Prototyp. J., 20, 192, 10.1108/RPJ-01-2013-0012
Wayne, 2014, Comparative evaluation of an open-source FDM system, Rapid Prototyp. J., 20, 205, 10.1108/RPJ-06-2012-0058
Zhang, 2019, Dynamic condition monitoring for 3D printers by using error fusion of multiple sparse auto-encoders, Comput. Ind., 105, 164, 10.1016/j.compind.2018.12.004
He, K., Yang, Z., Bai, Y., Long, J., and Li, C. (2018). Intelligent fault diagnosis of delta 3D printers using attitude sensors based on support vector machines. Sensors, 18.
Faes, M., Abbeloos, W., Vogeler, F., Valkenaers, H., Coppens, K., Goedemé, T., and Ferraris, E. (2016). Process monitoring of extrusion based 3D printing via laser scanning. arXiv.
Sirinukunwattana, 2016, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans. Med. Imag., 35, 1196, 10.1109/TMI.2016.2525803
Wu, 2019, Detecting cyber-physical attacks in CyberManufacturing systems with machine learning methods, J. Intell. Manuf., 30, 1111, 10.1007/s10845-017-1315-5
Ahuja, B., Karg, M., and Schmidt, M. (2015). Additive Manufacturing in Production: Challenges and Opportunities. Laser 3D Manufacturing II. Int. Soc. Optics Photonics, 9353.
(2020, May 06). Ultimaker 3 Manual (EN).pdf. Available online: https://ultimaker.com/download/61355/Ultimaker%203%20manual%20%28EN%29.pdf.
(2020, May 06). UM_HEROPlus_ENG_REVA_WEB.pdf. Available online: https://gopro.com/content/dam/help/heroplus/manuals/UM_HEROPlus_ENG_REVA_WEB.pdf.
(2020, May 06). Surface-Book-User-Guide-EN.pdf. Available online: https://download.microsoft.com/download/7/B/1/7B10C82E-F520-4080-8516-5CF0D803EEE0/surface-book-user-guide-EN.pdf.
(2020, May 06). What is R?. Available online: https://www.r-project.org/about.html.