Electrohydrodynamic printing process monitoring by microscopic image identification

Jie Sun, Linzhi Jing, Xiaotian Fan, Xueying Gao, Yung C.Liang

Article ID: 164
Vol 5, Issue 1, 2019, Article identifier:164

VIEWS - 685 (Abstract) 204 (PDF)


Electrohydrodynamic printing (EHDP) is able to precisely manipulate the position, size, and morphology of micro-/nano-fibers and fabricate high-resolution scaffolds using viscous biopolymer solutions. However, less attention has been paid to the influence of EHDP jet characteristics and key process parameters on deposited fiber patterns. To ensure the printing quality, it is very necessary to establish the relationship between the cone shapes and the stability of scaffold fabrication process. In this work, we used a digital microscopic imaging technique to monitor EHDP cones during printing, with subsequent image processing algorithms to extract related features, and a recognition algorithm to determine the suitability of Taylor cones for EHDP scaffold fabrication. Based on the experimental data, it has been concluded that the images of EHDP cone modes and the extracted features (centroid, jet diameter) are affected by their process parameters such as nozzle-substrate distance, the applied voltage, and stage moving speed. A convolutional neural network is then developed to classify these EHDP cone modes with the consideration of training time consumption and testing accuracy. A control algorithm will be developed to regulate the process parameters at the next stage for effective scaffold fabrication.


electrohydrodynamic jetting; convolutional neural network; image processing, scaffold fabrication

Full Text:



Zhang B, He J, Li X, et al., 2016, Micro/nanoscale electrohydrodynamic printing: From 2D to 3D. Nanoscale, 8(34): 15376–15388. https://doi.org/10.1039/c6nr04106j.

Park J U, Hardy M, Kang S J, et al., 2007, High–resolution electrohydrodynamic jet printing. Nat Mater, 6(10): 782–789. https://doi.org/10.1038/nmat1974.

Sun J, Vijayavenkataraman S, Liu H, 2017, An overview of scaffold design and fabrication technology for engineered knee meniscus. Materials, 10(1): 29. https://doi.org/10.3390/ ma10010029.

Garg K, Bowlin G L, 2011, Electrospinning jets and nanofibrous structures. Microfluidics, 5(1): 13403. https:// doi.org/10.1063/1.3567097.

Reneker D H, Yarin A L, 2008, Electrospinning jets and polymer nanofibers. Polymer, 49(10): 2387–2425. https://doi. org/10.1016/j.polymer.2008.02.002.

Cai Y, Gevelber M, 2013, The effect of relative humidity and evaporation rate on electrospinning: Fiber diameter and measurement for control implications. J Mater Sci, 48(22): 7812–7826. https://doi.org/10.1007/s10853–013–7544–x.

Yarin A L, Koombhongse S, Reneker D H, 2001, Bending instability in electrospinning of nanofibers. J Appl Phys, 89(5): 3018–3026.

Wang, X., 2016, Rheology behaviors of stable electrohydrodynamic direct–write jet. AIP Adv, 6(10): 105103.

Huang Y, Duan Y, Ding Y, et al., 2014, Versatile, kinetically controlled, high precision electrohydrodynamic writing of micro/nanofibers. Sci Rep, 4: 5949. https://doi.org/10.1038/ srep05949.

Park J, Park J W, Nasrabadi A M, et al., 2016, Methodology to set up nozzle–substrate gap for high resolution electrohydrodynamic jet printing. Appl Phys Lett, 109(13): 134104. https://doi.org/10.1063/1.4963846.

Gonzalez R C, Woods R E, Eddins S L, 2004, Digital Image Processing using MATLAB. Vol. 624. Upper Saddle River, New Jersey: Pearson–Prentice–Hall.

Reneker D H and Yarin, AL, 2008, Electrospinning jets and polymer nanofibers. Polymer, 49(10), 2387–2425.

Duan Y, Ding Y, Xu Z, et al., 2017, Helix electrohydrodynamic printing of highly aligned serpentine micro/nanofibers. Polymers, 9(9): 434.

Sun J, Hong G S, Rahman M, et al., 2005, Improved performance evaluation of tool condition identification by manufacturing loss consideration. Int J Prod Res, 43(6): 1185–1204.

Cireşan, D, Meier, U, Schmidhuber, J, 2012, Multi-Column Deep Neural Networks for Image Classification. 2012 IEEE Conf.Comput Vis Pattern Recognit, 2012: 3642-3649.

LeCun Y, Bengio Y, Hinton G, 2015, Deep learning. Nature, 521(7553): 436.

Fu J L, Zheng H L, Mei T, et al., 2017, Look closer to see better: Recurrent attention convolutional neural network for fine–grained image recognition. 2017 IEEE Conf.Comput Vis Pattern Recognit, 2017: 4476–4484. https://doi.org/10.1109/ CVPR.2017.476.

Simonyan K, Zisserman A, 2014, Very Deep Convolutional Networks for Large–scale Image Recognition. In Process International Conference on Learning Representations http:// arxiv.org/abs/1409.1556.

Raje P V, Murmu N C, 2014, A review on electrohydrodynamic– inkjet printing technology. Int J Emerg Technol Adv Eng, 4(5): 174–183.

Linzhi J, Xiang W, Hang L, et al., 2018, Zein Increases the cytoaffinity and biodegradability of scaffolds 3D–printed with zein and poly (ε–caprolactone) composite ink. ACS Appl Mater Interfaces, 10(22): 18551–18559. https: doi: 10.1021/ acsami.8b04344.

TensorFlow. Available from: https://www.tensorflow.org. [Last accessed on 2018 Jul 14].

Srivastava N, Hinton G, Krizhevsky A, et al., 2014, Dropout: A simple way to prevent neural networks from overfitting. J Mach Learn Res, 15(1): 1929–1958.

DOI: http://dx.doi.org/10.18063/ijb.v5i1.164


  • There are currently no refbacks.

Copyright (c) 2019 Sun J, et al.

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.