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.

Copyright © 2018 by ASME
Topics: Drops , Bioprinting , Modeling
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Fig. 1

Inkjet-based 3D bioprinting

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Fig. 2

A computational framework of ensemble learning

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Fig. 3

Schematic of the experimental setup

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Fig. 4

Excitation waveform

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Fig. 5

Droplet at pinch-off

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Fig. 6

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

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Fig. 7

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

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Fig. 8

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

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Fig. 9

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

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Fig. 10

Variable importance for the predictive model of droplet velocity

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Fig. 11

Variable importance for the predictive model of droplet volume



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