Unsupervised learning for the droplet evolution prediction and process dynamics understanding in inkjet printing

Singh, 2010, Inkjet printing-process and its applications, Adv. Mater., 22, 673, 10.1002/adma.200901141 Sun, 2015, Recent advances in controlling the depositing morphologies of inkjet droplets, ACS Appl. Mater. Interfaces, 7, 28086, 10.1021/acsami.5b07006 Mironov, 2003, Organ printing: computer-aided jet-based 3d tissue engineering, Trends Biotechnol., 21, 157, 10.1016/S0167-7799(03)00033-7 Sirringhaus, 2000, High-resolution inkjet printing of all-polymer transistor circuits, Science, 290, 2123, 10.1126/science.290.5499.2123 Yan, 2017, 3d printing hierarchical silver nanowire aerogel with highly compressive resilience and tensile elongation through tunable poisson's ratio, Small, 13, 1701756, 10.1002/smll.201701756 Park, 2010, Nanoscale, electrified liquid jets for high-resolution printing of charge, Nano Lett., 10, 584, 10.1021/nl903495f Tekin, 2008, Inkjet printing as a deposition and patterning tool for polymers and inorganic particles, Soft Matter, 4, 703, 10.1039/b711984d Pesach, 1987, Marangoni effects in the spreading of liquid mixtures on a solid, Langmuir, 3, 519, 10.1021/la00076a013 Hoath, 2016 Wijshoff, 2010, The dynamics of the piezo inkjet printhead operation, Phys. Rep., 491, 77, 10.1016/j.physrep.2010.03.003 Basaran, 2013, Nonstandard inkjets, Annu. Rev. Fluid Mech., 45, 85, 10.1146/annurev-fluid-120710-101148 Bartolo, 2007, Dynamics of non-newtonian droplets, Phys. Rev. Lett., 99, 174502, 10.1103/PhysRevLett.99.174502 Hill, 2005, Rheofluorescence technique for the study of dilute meh-ppv solutions in couette flow, J. Fluoresc., 15, 255, 10.1007/s10895-005-2625-0 Tsai, 2008, Effects of pulse voltage on inkjet printing of a silver nanopowder suspension, Nanotechnology, 19, 335304, 10.1088/0957-4484/19/33/335304 Barton, 2011, Control of high-resolution electrohydrodynamic jet printing, Control Eng. Pract., 19, 1266, 10.1016/j.conengprac.2011.05.009 Rao, 2015, Online real-time quality monitoring in additive manufacturing processes using heterogeneous sensors, J. Manuf. Sci. Eng., 137, 61007, 10.1115/1.4029823 Dong, 2006, Visualization of drop-on-demand inkjet: drop formation and deposition, Rev. Sci. Instrum., 77, 85101, 10.1063/1.2234853 Qin, 2019, In-process monitoring of electrohydrodynamic inkjet printing using machine vision, AIP Conference Proceedings, Vol. 2102, 70008 Xu, 2017, Study of pinch-off locations during drop-on-demand inkjet printing of viscoelastic alginate solutions, Langmuir, 33, 5037, 10.1021/acs.langmuir.7b00874 Wu, 2018, Predictive modeling of droplet formation processes in inkjet-based bioprinting, J. Manuf. Sci. Eng., 140, 10.1115/1.4040619 Gobert, 2018, Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging, Addit. Manuf., 21, 517 Scime, 2018, A multi-scale convolutional neural network for autonomous anomaly detection and classification in a laser powder bed fusion additive manufacturing process, Addit. Manuf., 24, 273 O’Reilly, 2014 Goroshin, 2015, Unsupervised learning of spatiotemporally coherent metrics, Proceedings of the IEEE International Conference on Computer Vision, 4086 Scime, 2018, Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm, Addit. Manuf., 19, 114 Yuan, 2019, Semi-supervised convolutional neural networks for in-situ video monitoring of selective laser melting, 744 Lotter, 2016 Rao, 1999, Predictive coding in the visual cortex: a functional interpretation of some extra-classical receptive-field effects, Nat. Neurosci., 2, 79, 10.1038/4580 Clark, 2013, Whatever next?. Predictive brains, situated agents, and the future of cognitive science, Behav. Brain Sci., 36, 181, 10.1017/S0140525X12000477 Chalasani, 2013 Everton, 2016, Review of in-situ process monitoring and in-situ metrology for metal additive manufacturing, Mater. Des., 95, 431, 10.1016/j.matdes.2016.01.099 Tapia, 2014, A review on process monitoring and control in metal-based additive manufacturing, J. Manuf. Sci. Eng., 136, 60801, 10.1115/1.4028540 Grasso, 2017, Process defects and in situ monitoring methods in metal powder bed fusion: a review, Meas. Sci. Technol., 28, 44005, 10.1088/1361-6501/aa5c4f Sun, 2017, Functional quantitative and qualitative models for quality modeling in a fused deposition modeling process, IEEE Trans. Autom. Sci. Eng., 15, 393, 10.1109/TASE.2017.2763609 Lane, 2016, Thermographic measurements of the commercial laser powder bed fusion process at nist, Rapid Prototyp. J., 22, 778, 10.1108/RPJ-11-2015-0161 Bertoli, 2017, In-situ characterization of laser-powder interaction and cooling rates through high-speed imaging of powder bed fusion additive manufacturing, Mater. Des., 135, 385, 10.1016/j.matdes.2017.09.044 Shevchik, 2018, Acoustic emission for in situ quality monitoring in additive manufacturing using spectral convolutional neural networks, Addit. Manuf., 21, 598 Kanko, 2016, In situ morphology-based defect detection of selective laser melting through inline coherent imaging, J. Mater. Process. Technol., 231, 488, 10.1016/j.jmatprotec.2015.12.024 Wang, 2017, Residual stress mapping in inconel 625 fabricated through additive manufacturing: method for neutron diffraction measurements to validate thermomechanical model predictions, Mater. Des., 113, 169, 10.1016/j.matdes.2016.10.003 Sitthi-Amorn, 2015, Multifab: a machine vision assisted platform for multi-material 3d printing, ACM Trans. Graph. (TOG), 34, 129, 10.1145/2766962 Yang, 2013, High viscosity jetting system for 3d reactive inkjet printing, Twenty Forth Annual International Solid Freeform Fabrication Symposium – An Additive Manufacturing Conference, 505 Kwon, 2012, Low-cost and high speed monitoring system for a multi-nozzle piezo inkjet head, Sens. Actuators A: Phys., 180, 154, 10.1016/j.sna.2012.04.009 Wang, 2017, Low-cost and in-situ droplet micro-sensing for inkjet 3d printing quality assurance, Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems, 27 Wang, 2019, Online droplet monitoring in inkjet 3d printing using catadioptric stereo system, IISE Trans., 51, 153, 10.1080/24725854.2018.1532133 Wang, 2018, In-situ droplet inspection and closed-loop control system using machine learning for liquid metal jet printing, J. Manuf. Syst., 47, 83, 10.1016/j.jmsy.2018.04.003 Razvi, 2019, A review of machine learning applications in additive manufacturing, International Design Engineering Technical Conferences & Computers and Information in Engineering Conference Gobert, 2018, Application of supervised machine learning for defect detection during metallic powder bed fusion additive manufacturing using high resolution imaging, Addit. Manuf., 21, 517 Okaro, 2019, Automatic fault detection for laser powder-bed fusion using semi-supervised machine learning, Addit. Manuf., 27, 42 Scime, 2018, Anomaly detection and classification in a laser powder bed additive manufacturing process using a trained computer vision algorithm, Addit. Manuf., 19, 114 Khanzadeh, 2019, In-situ monitoring of melt pool images for porosity prediction in directed energy deposition processes, IISE Trans., 51, 437, 10.1080/24725854.2017.1417656 Grasso, 2018, Data fusion methods for statistical process monitoring and quality characterization in metal additive manufacturing, Proc. CIRP, 75, 103, 10.1016/j.procir.2018.04.045 Stetco, 2019, Machine learning methods for wind turbine condition monitoring: a review, Renew. Energy, 133, 620, 10.1016/j.renene.2018.10.047 Qin, 2012, Survey on data-driven industrial process monitoring and diagnosis, Annu. Rev. Control, 36, 220, 10.1016/j.arcontrol.2012.09.004 Zhao, 2019, Deep learning and its applications to machine health monitoring, Mech. Syst. Signal Process., 115, 213, 10.1016/j.ymssp.2018.05.050 Sarrazin, 2006, Experimental and numerical study of droplets hydrodynamics in microchannels, AIChE J., 52, 4061, 10.1002/aic.11033 Shinjo, 2010, Simulation of liquid jet primary breakup: dynamics of ligament and droplet formation, Int. J. Multiph. Flow, 36, 513, 10.1016/j.ijmultiphaseflow.2010.03.008 Kim, 2012, Numerical study on the effects of non-dimensional parameters on drop-on-demand droplet formation dynamics and printability range in the up-scaled model, Phys. Fluids, 24, 82103, 10.1063/1.4742913 Groot Wassink, 2007 Chorin, 1968, Numerical solution of the Navier–Stokes equations, Math. Comput., 22, 745, 10.1090/S0025-5718-1968-0242392-2 Santner, 2003, 1 Wang, 2017, In-situ droplet inspection and control system for liquid metal jet 3d printing process, Proc. Manuf., 10, 968 Srivastava, 2015, Unsupervised learning of video representations using lstms, International Conference on Machine Learning, 843 Finn, 2016, Unsupervised learning for physical interaction through video prediction, 64 Xingjian, 2015, Convolutional lstm network: a machine learning approach for precipitation nowcasting, 802 Kingma, 2014 Wang, 2004, Image quality assessment: from error visibility to structural similarity, IEEE Trans. Image Process., 13, 600, 10.1109/TIP.2003.819861 Saxe, 2011, On random weights and unsupervised feature learning, ICML, Vol. 2, 6