0
Research Papers

Predictive Modeling of Droplet Formation Processes in Inkjet-Based Bioprinting

[+] Author and Article Information
Dazhong Wu

Department of Mechanical and
Aerospace Engineering,
Department of Industrial Engineering and
Management Systems,
University of Central Florida,
Orlando, FL 32816
e-mail: Dazhong.Wu@ucf.edu

Changxue Xu

Department of Industrial,
Manufacturing, and Systems Engineering,
Texas Tech University,
Lubbock, TX 79409
e-mail: changxue.xu@ttu.edu

1Corresponding authors.

Manuscript received April 23, 2018; final manuscript received June 16, 2018; published online July 9, 2018. Assoc. Editor: Zhijian J. Pei.

J. Manuf. Sci. Eng 140(10), 101007 (Jul 09, 2018) (9 pages) Paper No: MANU-18-1269; doi: 10.1115/1.4040619 History: Received April 23, 2018; Revised June 16, 2018

Additive manufacturing is driving major innovations in many areas such as biomedical engineering. Recent advances have enabled three-dimensional (3D) printing of biocompatible materials and cells into complex 3D functional living tissues and organs using bio-printable materials (i.e., bioink). Inkjet-based bioprinting fabricates the tissue and organ constructs by ejecting droplets onto a substrate. Compared with microextrusion-based and laser-assisted bioprinting, it is very difficult to predict and control the droplet formation process (e.g., droplet velocity and volume). To address this issue, this paper presents a new data-driven approach to predicting droplet velocity and volume in the inkjet-based bioprinting process. An imaging system was used to monitor the droplet formation process. To investigate the effects of polymer concentration, excitation voltage, dwell time, and rise time on droplet velocity and volume, a full factorial design of experiments (DOE) was conducted. Two predictive models were developed to predict droplet velocity and volume using ensemble learning. The accuracy of the two predictive models was measured using the root-mean-square error (RMSE), relative error (RE), and coefficient of determination (R2). Experimental results have shown that the predictive models are capable of predicting droplet velocity and volume with sufficient accuracy.

FIGURES IN THIS ARTICLE
<>
Copyright © 2018 by ASME
Topics: Drops , Bioprinting , Modeling
Your Session has timed out. Please sign back in to continue.

References

Guillemot, F. , Mironov, V. , and Nakamura, M. , 2010, “ Bioprinting Is Coming of Age: Report From the International Conference on Bioprinting and Biofabrication in Bordeaux (3B'09),” Biofabrication, 2(1), p. 010201. [CrossRef] [PubMed]
Norotte, C. , Marga, F. S. , Niklason, L. E. , and Forgacs, G. , 2009, “ Scaffold-Free Vascular Tissue Engineering Using Bioprinting,” Biomaterials, 30(30), pp. 5910–5917. [CrossRef] [PubMed]
Xu, C. , Chai, W. , Huang, Y. , and Markwald, R. R. , 2012, “ Scaffold‐Free Inkjet Printing of Three‐Dimensional Zigzag Cellular Tubes,” Biotechnol. Bioeng., 109(12), pp. 3152–3160. [CrossRef] [PubMed]
Xu, C. , Zhang, Z. , Christensen, K. , Huang, Y. , Fu, J. , and Markwald, R. R. , 2014, “ Freeform Vertical and Horizontal Fabrication of Alginate-Based Vascular-like Tubular Constructs Using Inkjetting,” ASME J. Manuf. Sci. Eng., 136(6), p. 061020. [CrossRef]
Dababneh, A. B. , and Ozbolat, I. T. , 2014, “ Bioprinting Technology: A Current State-of-the-Art Review,” ASME J. Manuf. Sci. Eng., 136(6), p. 061016. [CrossRef]
Ozbolat, I. T. , and Hospodiuk, M. , 2016, “ Current Advances and Future Perspectives in Extrusion-Based Bioprinting,” Biomaterials, 76, pp. 321–343. [CrossRef] [PubMed]
Murphy, S. V. , and Atala, A. , 2014, “ 3D Bioprinting of Tissues and Organs,” Nat. Biotechnol., 32(8), pp. 773–785. [CrossRef] [PubMed]
Herran, C. L. , Wang, W. , Huang, Y. , Mironov, V. , and Markwald, R. , 2010, “ Parametric Study of Acoustic Excitation-Based Glycerol-Water Microsphere Fabrication in Single Nozzle Jetting,” ASME J. Manuf. Sci. Eng., 132(5), p. 051001. [CrossRef]
Herran, C. L. , and Huang, Y. , 2012, “ Alginate Microsphere Fabrication Using Bipolar Wave-Based Drop-on-Demand Jetting,” J. Manuf. Processes, 14(2), pp. 98–106. [CrossRef]
Hon, K. , Li, L. , and Hutchings, I. , 2008, “ Direct Writing Technology—Advances and Developments,” CIRP Ann., 57(2), pp. 601–620. [CrossRef]
Ozbolat, I. T. , 2015, “ Scaffold-Based or Scaffold-Free Bioprinting: Competing or Complementing Approaches?,” ASME J. Nanotechnol. Eng. Med., 6(2), p. 024701. [CrossRef]
Hospodiuk, M. , Dey, M. , Sosnoski, D. , and Ozbolat, I. T. , 2017, “ The Bioink: A Comprehensive Review on Bioprintable Materials,” Biotechnol. Adv., 35(2), pp. 217–239. [CrossRef] [PubMed]
Gudapati, H. , Dey, M. , and Ozbolat, I. , 2016, “ A Comprehensive Review on Droplet-Based Bioprinting: Past, Present and Future,” Biomaterials, 102, pp. 20–42. [CrossRef] [PubMed]
Duan, B. , 2017, “ State-of-the-Art Review of 3D Bioprinting for Cardiovascular Tissue Engineering,” Ann. Biomed. Eng., 45(1), pp. 195–209. [CrossRef] [PubMed]
Tsai, M.-H. , Hwang, W.-S. , and Chou, H. , 2009, “ The Micro-Droplet Behavior of a Molten Lead-Free Solder in an Inkjet Printing Process,” J. Micromech. Microeng., 19(12), p. 125021. [CrossRef]
Derby, B. , and Reis, N. , 2003, “ Inkjet Printing of Highly Loaded Particulate Suspensions,” MRS Bull., 28(11), pp. 815–818. [CrossRef]
Wang, T. , and Derby, B. , 2005, “ Ink‐Jet Printing and Sintering of PZT,” J. Am. Ceram. Soc., 88(8), pp. 2053–2058. [CrossRef]
Wang, X. , Carr, W. W. , Bucknall, D. G. , and Morris, J. F. , 2012, “ Drop-on-Demand Drop Formation of Colloidal Suspensions,” Int. J. Multiphase Flow, 38(1), pp. 17–26. [CrossRef]
Xu, C. , Zhang, M. , Huang, Y. , Ogale, A. , Fu, J. , and Markwald, R. R. , 2014, “ Study of Droplet Formation Process During Drop-on-Demand Inkjetting of Living Cell-Laden Bioink,” Langmuir, 30(30), pp. 9130–9138. [CrossRef] [PubMed]
Zhang, M. , Krishnamoorthy, S. , Song, H. , Zhang, Z. , and Xu, C. , 2017, “ Ligament Flow During Drop-on-Demand Inkjet Printing of Bioink Containing Living Cells,” J. Appl. Phys., 121(12), p. 124904. [CrossRef]
Christensen, K. , Xu, C. , Chai, W. , Zhang, Z. , Fu, J. , and Huang, Y. , 2015, “ Freeform Inkjet Printing of Cellular Structures With Bifurcations,” Biotechnol. Bioeng., 112(5), pp. 1047–1055. [CrossRef] [PubMed]
Xu, C. , Huang, Y. , Fu, J. , and Markwald, R. R. , 2014, “ Electric Field-Assisted Droplet Formation Using Piezoactuation-Based Drop-on-Demand Inkjet Printing,” J. Micromech. Microeng., 24(11), p. 115011. [CrossRef]
Breiman, L. , 2001, “ Random Forests,” Mach. Learn., 45(1), pp. 5–32. [CrossRef]
Liaw, A. , and Wiener, M. , 2002, “ Classification and Regression by Random Forest,” R. News, 2(3), pp. 18–22.
Wu, D. , Jennings, C. , Terpenny, J. , Gao, R. X. , and Kumara, S. , 2017, “ A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests,” ASME J. Manuf. Sci. Eng., 139(7), p. 071018. [CrossRef]
Tibshirani, R. , 1996, “ Regression Shrinkage and Selection Via the Lasso,” J. R. Stat. Soc. Ser. B (Methodological), 58(1), pp. 267–288.
Cortes, C. , and Vapnik, V. , 1995, “ Support-Vector Networks,” Mach. Learn., 20(3), pp. 273–297.
Schölkopf, B. , and Smola, A. J. , 2002, Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT press, Cambridge, MA.
Smola, A. J. , and Schölkopf, B. , 2004, “ A Tutorial on Support Vector Regression,” Stat. Comput., 14(3), pp. 199–222. [CrossRef]
Lawson, C. L. , and Hanson, R. J. , 1995, Solving Least Squares Problems, Siam, Philadelphia, PA. [CrossRef]
Zhang, Z. , Xu, C. , Xiong, R. , Chrisey, D. B. , and Huang, Y. , 2017, “ Effects of Living Cells on the Bioink Printability During Laser Printing,” Biomicrofluidics, 11(3), p. 034120. [CrossRef] [PubMed]
Ding, H. , Dai, E. , Tourlomousis, F. , and Chang, R. C. , 2017, “ A Methodology for Quantifying Cell Density and Distribution in Multidimensional Bioprinted Gelatin-Alginate Constructs,” ASME Paper No. MSEC2017-2853.
Yu, Y. , Zhang, Y. , and Ozbolat, I. T. , 2014, “ A Hybrid Bioprinting Approach for Scale-Up Tissue Fabrication,” ASME J. Manuf. Sci. Eng., 136(6), p. 061013. [CrossRef]
Nishiyama, Y. , Nakamura, M. , Henmi, C. , Yamaguchi, K. , Mochizuki, S. , Nakagawa, H. , and Takiura, K. , 2009, “ Development of a Three-Dimensional Bioprinter: Construction of Cell Supporting Structures Using Hydrogel and State-of-the-Art Inkjet Technology,” ASME J. Biomech. Eng., 131(3), p. 035001. [CrossRef]
Michael, S. , Sorg, H. , Peck, C.-T. , Koch, L. , Deiwick, A. , Chichkov, B. , Vogt, P. M. , and Reimers, K. , 2013, “ Tissue Engineered Skin Substitutes Created by Laser-Assisted Bioprinting Form Skin-like Structures in the Dorsal Skin Fold Chamber in Mice,” PloS One, 8(3), p. e57741. [CrossRef] [PubMed]
Merceron, T. K. , Burt, M. , Seol, Y.-J. , Kang, H.-W. , Lee, S. J. , Yoo, J. J. , and Atala, A. , 2015, “ A 3D Bioprinted Complex Structure for Engineering the Muscle–Tendon Unit,” Biofabrication, 7(3), p. 035003. [CrossRef] [PubMed]
Wu, D. , Rosen, D. W. , Wang, L. , and Schaefer, D. , 2015, “ Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation,” Comput.-Aided Des., 59, pp. 1–14. [CrossRef]
Rayleigh, L. , 1878, “ On the Instability of Jets,” Proc. London Math. Soc., 1(1), pp. 4–13. [CrossRef]
Bogy, D. , 1979, “ Drop Formation in a Circular Liquid Jet,” Annu. Rev. Fluid Mech., 11(1), pp. 207–228. [CrossRef]

Figures

Grahic Jump Location
Fig. 1

Inkjet-based 3D bioprinting

Grahic Jump Location
Fig. 2

A computational framework of ensemble learning

Grahic Jump Location
Fig. 3

Schematic of the experimental setup

Grahic Jump Location
Fig. 4

Excitation waveform

Grahic Jump Location
Fig. 5

Droplet at pinch-off

Grahic Jump Location
Fig. 6

Observed versus predicted droplet velocity (training data: 60%)

Grahic Jump Location
Fig. 7

Observed versus predicted droplet volume (training data: 60%)

Grahic Jump Location
Fig. 8

Observed versus predicted droplet velocity (training data: 80%)

Grahic Jump Location
Fig. 9

Observed versus predicted droplet volume (training data: 80%)

Grahic Jump Location
Fig. 10

Variable importance for the predictive model of droplet velocity

Grahic Jump Location
Fig. 11

Variable importance for the predictive model of droplet volume

Tables

Errata

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In