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Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine Learning Guided Parameter Recommendation
Journal article   Open access

Evaluation of Printing Parameters on 3D Extrusion Printing of Pluronic Hydrogels and Machine Learning Guided Parameter Recommendation

Zhouquan Fu, Vincent Angeline and Wei Sun
INTERNATIONAL JOURNAL OF BIOPRINTING, v 7(4), pp 179-189
01 Jan 2021
DOI: https://doi.org/10.18063/IJB.V7I4.434
PMID: 34805600
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.18063/ijb.v7i4.434View
Published, Version of Record (VoR)CC BY V4.0,  Open
url
https://doi.org/10.18063/IJB.V7I4.434View
Published, Version of Record (VoR) Open

Abstract

Engineering Engineering, Biomedical Materials Science Materials Science, Biomaterials Science & Technology Technology
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.

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11 Record Views
64 citations in Web of Science
66 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#9 Industry, Innovation and Infrastructure

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Engineering, Biomedical
Materials Science, Biomaterials
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