TY - JOUR
T1 - Predictive modeling of droplet formation processes in inkjet-based bioprinting
AU - Wu, Dazhong
AU - Xu, Changxue
N1 - Publisher Copyright:
Copyright © 2018 by ASME.
PY - 2018/10
Y1 - 2018/10
N2 - Additive manufacturing is driving major innovations in many areas such as biomedical engineering. Recent advances have enabled three-dimensional (3D) printing of biocom-patible 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.
AB - Additive manufacturing is driving major innovations in many areas such as biomedical engineering. Recent advances have enabled three-dimensional (3D) printing of biocom-patible 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.
KW - Droplet formation
KW - Droplet velocity
KW - Droplet volume
KW - Inkjet-based bioprinting
KW - Predictive modeling
UR - http://www.scopus.com/inward/record.url?scp=85051076331&partnerID=8YFLogxK
U2 - 10.1115/1.4040619
DO - 10.1115/1.4040619
M3 - Article
AN - SCOPUS:85051076331
SN - 1087-1357
VL - 140
JO - Journal of Manufacturing Science and Engineering, Transactions of the ASME
JF - Journal of Manufacturing Science and Engineering, Transactions of the ASME
IS - 10
M1 - 101007
ER -