Predictive modeling of droplet formation processes in inkjet-based bioprinting

Dazhong Wu, Changxue Xu

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

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.

Original languageEnglish
Article number101007
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Volume140
Issue number10
DOIs
StatePublished - Oct 2018

Keywords

  • Droplet formation
  • Droplet velocity
  • Droplet volume
  • Inkjet-based bioprinting
  • Predictive modeling

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