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《工程(英文)》 >> 2019年 第5卷 第3期 doi: 10.1016/j.eng.2018.12.009

基于机器学习的多目标优化设计在按需喷墨生物打印中的应用

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

 

收稿日期: 2018-09-08 修回日期: 2018-12-01 录用日期: 2018-12-25 发布日期: 2019-06-10

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摘要

按需滴化微喷射(DOD)生物打印技术以其高通量效率和高成本效益在组织工程中得到了广泛的应用。然而,这种类型的生物打印技术面临诸如星形液滴产生、过大的液滴生成和过低的液滴速度等问题。这些问题降低了DOD打印技术的稳定性和精度,打乱了细胞排列,进一步产生结构误差。为了解决这些问题,本文提出了一种基于全连接神经网络(FCNN)的DOD打印参数多目标优化(MOO)设计方法。该MOO问题包括两个目标函数:利用FCNN开发星形液滴产生模型;减小液滴直径,提高液滴速度。为了寻找MOO 问题的帕累托最优解集,本文提出了结合采用Adam算法的混合子梯度下降束法(MSGDB),并采用基于自适应学习速率算法的混合子梯度下降束法(HMSGDBA)。通过与MSGDB的比较研究,证明了HMSGDBA 的优越性。实验结果表明,使用该方法优化可得到稳定的单滴打印过程,液滴速度由0.88 m·s–1 提高到2.08 m·s–1。该方法能提高打印精度和稳定性,对实现精密细胞阵列和复杂的生物功能具有重要意义。此外,对细胞打印实验平台的搭建具有指导意义。

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