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Engineering    2019, Vol. 5 Issue (3) : 586 -593     https://doi.org/10.1016/j.eng.2018.12.009
Research Drop-on-Demand Printing—Article |
Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting
Jia Shiab, Jinchun Songa, Bin Songc, Wen F. Lub()
a School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
b Department of Mechanical Engineering, National University of Singapore, Singapore 119077, Singapore
c Singapore Institute of Manufacturing Technology, Singapore 637662, Singapore
Abstract
Abstract  Abstract

Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its high-throughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs; and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multi-subgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s−1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms.

Keywords Drop-on-demand printing      Inkjet printing      Gradient descent multi-objective optimization      Fully connected neural networks     
Corresponding Authors: Wen F. Lu   
Issue Date: 11 July 2019
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Jia Shi
Jinchun Song
Bin Song
Wen F. Lu
Cite this article:   
Jia Shi,Jinchun Song,Bin Song, et al. Multi-Objective Optimization Design through Machine Learning for Drop-on-Demand Bioprinting[J]. Engineering, 2019, 5(3): 586 -593 .
URL:  
http://www.engineering.org.cn/EN/10.1016/j.eng.2018.12.009     OR     http://www.engineering.org.cn/EN/Y2019/V5/I3/586
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