Towards Design Automation of Microfluidic Mixers: Leveraging Reinforcement Learning and Artificial Neural Networks.

Area of Science:

• Microfluidics and Lab-on-a-Chip Technology
• Artificial Intelligence in Engineering Design
• Computational Fluid Dynamics

Background:

• Microfluidic mixers are essential for sample amalgamation in microscale devices.
• Manual design of microfluidic mixers is complex, time-consuming, and requires specialized expertise.
• Intelligent automation is sought to streamline the design process of microfluidic mixers.

Purpose of the Study:

• To develop an automated approach for designing dimensional parameters of microfluidic mixers.
• To integrate artificial neural networks (ANNs) with reinforcement learning (RL) for automated design.
• To offer a precise, cost-efficient, and rapid alternative to conventional simulation methods.

Main Methods:

• Trained two precise and cost-efficient neural network models using 10,000 COMSOL simulation datasets.
• Employed reinforcement learning agents with effective state evaluation functions for automated parameter design.
• Tested the automated design approach on two typical microfluidic mixer structures.

Main Results:

• The automated design process successfully optimized dimensional parameters for both tested microfluidic mixer structures.
• The first mixer model was optimized in 0.129 seconds, and the second in 0.169 seconds.
• Achieved significant time reduction compared to traditional manual design methods.

Conclusions:

• Reinforcement learning techniques show significant potential for the automated design of microfluidic mixers.
• The developed approach offers a novel, efficient solution for microfluidic device design.
• This method drastically cuts down design time, enhancing the practicality of microfluidic technology.