Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches
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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Online Access: | https://hdl.handle.net/10356/169343 |
| _version_ | 1826109463668457472 |
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| author | Huang,Xi Ng, Wei Long Yeong, Wai Yee |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Huang,Xi Ng, Wei Long Yeong, Wai Yee |
| author_sort | Huang,Xi |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T02:18:39Z |
| format | Journal Article |
| id | ntu-10356/169343 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:18:39Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1693432023-07-15T16:48:11Z Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches Huang,Xi Ng, Wei Long Yeong, Wai Yee School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing HP-NTU Digital Manufacturing Corporate Lab Engineering::Mechanical engineering 3D Printing Biofabrication 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. Submitted/Accepted version This study is supported under the RIE2020 Industry Alignment Fund – Industry Collaboration Projects (IAF-ICP) Funding Initiative, as well as cash and in-kind contribution from the industry partner, HP Inc., through the HP-NTU Digital Manufacturing Corporate Lab. 2023-07-13T07:32:44Z 2023-07-13T07:32:44Z 2023 Journal Article 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 0956-5515 https://hdl.handle.net/10356/169343 10.1007/s10845-023-02167-4 2-s2.0-85162072940 en Journal of Intelligent Manufacturing © 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. application/pdf |
| spellingShingle | Engineering::Mechanical engineering 3D Printing Biofabrication Huang,Xi Ng, Wei Long Yeong, Wai Yee Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title | Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title_full | Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title_fullStr | Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title_full_unstemmed | Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title_short | Predicting the number of printed cells during inkjet-based bioprinting process based on droplet velocity profile using machine learning approaches |
| title_sort | predicting the number of printed cells during inkjet based bioprinting process based on droplet velocity profile using machine learning approaches |
| topic | Engineering::Mechanical engineering 3D Printing Biofabrication |
| url | https://hdl.handle.net/10356/169343 |
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