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Open AccessArticle
Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks
by
Yuwei Chen
Yuwei Chen †,
Taotao Sun
Taotao Sun †,
Zhenya Liu
Zhenya Liu ,
Yidan Zhang
Yidan Zhang and
Junchao Wang
Junchao Wang *
School of Integrated Circuit Science and Engineering, Hangzhou Dianzi University, Hangzhou 310018, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Micromachines 2024, 15(7), 901; https://doi.org/10.3390/mi15070901 (registering DOI)
Submission received: 15 May 2024
/
Revised: 24 June 2024
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Accepted: 8 July 2024
/
Published: 10 July 2024
Abstract
Microfluidic mixers, a pivotal application of microfluidic technology, are primarily utilized for the rapid amalgamation of diverse samples within microscale devices. Given the intricacy of their design processes and the substantial expertise required from designers, the intelligent automation of microfluidic mixer design has garnered significant attention. This paper discusses an approach that integrates artificial neural networks (ANNs) with reinforcement learning techniques to automate the dimensional parameter design of microfluidic mixers. In this study, we selected two typical microfluidic mixer structures for testing and trained two neural network models, both highly precise and cost-efficient, as alternatives to traditional, time-consuming finite-element simulations using up to 10,000 sets of COMSOL simulation data. By defining effective state evaluation functions for the reinforcement learning agents, we utilized the trained agents to successfully validate the automated design of dimensional parameters for these mixer structures. The tests demonstrated that the first mixer model could be automatically optimized in just 0.129 s, and the second in 0.169 s, significantly reducing the time compared to manual design. The simulation results validated the potential of reinforcement learning techniques in the automated design of microfluidic mixers, offering a new solution in this field.
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MDPI and ACS Style
Chen, Y.; Sun, T.; Liu, Z.; Zhang, Y.; Wang, J.
Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks. Micromachines 2024, 15, 901.
https://doi.org/10.3390/mi15070901
AMA Style
Chen Y, Sun T, Liu Z, Zhang Y, Wang J.
Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks. Micromachines. 2024; 15(7):901.
https://doi.org/10.3390/mi15070901
Chicago/Turabian Style
Chen, Yuwei, Taotao Sun, Zhenya Liu, Yidan Zhang, and Junchao Wang.
2024. "Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks" Micromachines 15, no. 7: 901.
https://doi.org/10.3390/mi15070901
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