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RESEARCH ARTICLE

Evaluation of Printing Parameters on 3D Extrusion

Printing of Pluronic Hydrogels and Machine Learning

Guided Parameter Recommendation

Zhouquan Fu , Vincent Angeline , Wei Sun *
1,2
1†
1†
1 Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA 19104, USA
2 Department of Mechanical Engineering, Tsinghua University, Beijing 100084, People’s Republic of China
†These authors contributed equally to this work

Abstract: Bioprinting is an emerging technology for the construction of complex three-dimensional (3D) constructs used
in various biomedical applications. One of the challenges in this field is the delicate manipulation of material properties and
various disparate printing parameters to create structures with high fidelity. Understanding the effects of certain parameters
and identifying optimal parameters for creating highly accurate structures are therefore a worthwhile subject to investigate.
The objective of this study is to investigate high-impact print parameters on the printing printability and develop a preliminary
machine learning model to optimize printing parameters. The results of this study will lead to an exploration of machine
learning applications in bioprinting and to an improved understanding between 3D printing parameters and structural
printability. Reported results include the effects of rheological property, nozzle gauge, nozzle temperature, path height, and ink
composition on the printability of Pluronic F127. The developed Support Vector Machine model generated a process map to
assist the selection of optimal printing parameters to yield high quality prints with high probability (>75%). Future work with
more generalized machine learning models in bioprinting is also discussed in this article. The finding of this study provides a
simple tool to improve printability of extrusion-based bioprinting with minimum experimentations.
Keywords: 3D printing; Bioprinting; Printability; Machine learning; Support vector machine; Pluronic

*Correspondence to: Wei Sun, Department of Mechanical Engineering and Mechanics, Drexel University, Philadelphia, PA 19104, USA;
sunwei@drexel.edu
Received: August 19, 2021; Accepted: September 3, 2021; Published Online: October 15, 2021

Citation: Fu Z, Angeline V, Sun W, 2021, Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine
Learning Guided Parameter Recommendation. Int J Bioprint, 7(4):434. http://doi.org/10.18063/ijb.v7i4.434

1. Introduction cure polymers of cell-hydrogel suspensions into 3D
structures. This method can achieve high resolution but
Three-dimensional (3D) bioprinting is a developing is limited to use with photo responsive bioinks . In ink
[8]
technique in the biomedical field for creating a wide range jetting, droplets of material containing cells are deposited
of biological 3D structures, including cell-laden constructs to form printed structures. Variation of droplet sizes and
and scaffolds . The bioprinting technique includes cell concentrations allows for control over concentrations
[1]
several processes, for example, computer-aided design within structures and high resolution. However, low
(CAD), 3D printing, and the synthesis of biomaterial viscosity of the bioinks is often required for the jetting
and living material . Bioprinting has found widespread process . Extrusion bioprinting is a bioprinting method
[2]
[9]
applications such as tissue engineering [3,4] , reconstructive derived from traditional thermoplastic 3D printing,
surgery , and drug delivery and screening [6,7] . Bioprinting incorporating a reservoir and nozzle through which
[5]
methods can be generally divided as extrusion, material material is extruded layer-by-layer onto a platform. It is
jetting, and vat polymerization. Vat polymerization unable to reach the resolutions achieved by other methods
utilizes stereolithography or digital light processing to due to limitations based on nozzle size, but it is low-cost,

© 2021 Fu, et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution License, permitting distribution and
reproduction in any medium, provided the original work is cited.
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