Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/169343
Title: Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches
Authors: Huang,Xi
Ng, Wei Long
Yeong, Wai Yee
Keywords: Engineering::Mechanical engineering
Issue Date: 2023
Source: Huang, X., Ng, W. L. & Yeong, W. Y. (2023). Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches. Journal of Intelligent Manufacturing. https://dx.doi.org/10.1007/s10845-023-02167-4
Journal: Journal of Intelligent Manufacturing 
Abstract: In this work, our proof-of-concept study can be used to predict the number of cells within printed droplets based on droplet velocity at two different points along the nozzle-substrate distance using machine learning approaches. A novel high-throughput contactless method that combines the use of an optical system and machine learning algorithms was utilized for various applications such as cell detection within single droplets (presence/absence of cells) and prediction of the total number of printed cells within multiple droplets by measuring the droplet deceleration between two positions along the nozzle-substrate distance. The proposed method in this work has demonstrated good accuracy in cell prediction within single droplet (presence/absence of cells) and low prediction error in determining number of cells within multiple droplets by reducing the error by a factor of (Fomrula Presented.). for N droplets measured in a batch. The performance of five different machine learning algorithms such as linear regression, support vector regression, decision tree regressor, random forest regression, and extra tree regression were compared to determine the best algorithm for each type of application. The random forest regressor algorithm demonstrated the highest accuracy at 80% in cell prediction (presence/absence of cells) within single droplets, while the extra tree regressor demonstrated the lowest mean error of 12% in predicting the number of printed cells within multiple droplets (e.g., 20 droplets on same spot). By incorporating these models in a droplet monitoring system, live assessment of the number of printed cells during an inkjet-based bioprinting process can be achieved.
URI: https://hdl.handle.net/10356/169343
ISSN: 0956-5515
DOI: 10.1007/s10845-023-02167-4
Schools: School of Mechanical and Aerospace Engineering 
Research Centres: Singapore Centre for 3D Printing 
HP-NTU Digital Manufacturing Corporate Lab
Rights: © 2023 The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. All rights reserved. This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s10845-023-02167-4.
Fulltext Permission: embargo_20240624
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles
SC3DP Journal Articles

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