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Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing

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Abstract

Drop-on-demand (DOD) printing is widely used in bioprinting for tissue engineering because of little damage to cell viability and cost-effectiveness. However, satellite droplets may be generated during printing, deviating cells from the desired position and affecting printing position accuracy. Current control on cell injection in DOD printing is primarily based on trial-and-error process, which is time-consuming and inflexible. In this paper, a novel machine learning technology based on Learning-based Cell Injection Control (LCIC) approach is demonstrated for effective DOD printing control while eliminating satellite droplets automatically. The LCIC approach includes a specific computational fluid dynamics (CFD) simulation model of piezoelectric DOD print-head considering inverse piezoelectric effect, which is used instead of repetitive experiments to collect data, and a multilayer perceptron (MLP) network trained by simulation data based on artificial neural network algorithm, using the well-known classification performance of MLP to optimize DOD printing parameters automatically. The test accuracy of the LCIC method was 90%. With the validation of LCIC method by experiments, satellite droplets from piezoelectric DOD printing are reduced significantly, improving the printing efficiency drastically to satisfy requirements of manufacturing precision for printing complex artificial tissues. The LCIC method can be further used to optimize the structure of DOD print-head and cell behaviors.

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Acknowledgment

Jia Shi thanks the China Scholarship Council (CSC) for the financial support of a visiting program at National University of Singapore.

Conflict of interest

The authors declare that they have no financial or personal relationships with other people or organizations that could inappropriately have influenced their work.

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Authors and Affiliations

  1. School of Mechanical Engineering and Automation, Northeastern University, Shenyang, Liaoning, China

    Jia Shi & Jinchun Song

  2. Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore, 1197576, Singapore

    Jia Shi, Bin Wu & Wen F. Lu

  3. Singapore Institute of Manufacturing Technology, Singapore, Singapore

    Bin Song

  4. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Shihao Li & Dieter Trau

Authors
  1. Jia Shi
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  2. Bin Wu
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  3. Bin Song
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  4. Jinchun Song
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  5. Shihao Li
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  6. Dieter Trau
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  7. Wen F. Lu
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Corresponding author

Correspondence to Wen F. Lu.

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Associate Editor Smadar Cohen oversaw the review of this article.

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Shi, J., Wu, B., Song, B. et al. Learning-Based Cell Injection Control for Precise Drop-on-Demand Cell Printing. Ann Biomed Eng 46, 1267–1279 (2018). https://doi.org/10.1007/s10439-018-2054-2

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  • DOI: https://doi.org/10.1007/s10439-018-2054-2

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