Adaptive Electrospinning System Based on Reinforcement Learning for Uniform-Thickness Nanofiber Air Filters

Electrospinning is a simple and versatile method to produce nanofiber filters. However, owing to bending instability that occurs during the electrospinning process, electrospinning has frequently produced a non-uniform-thickness nanofiber filter, which deteriorates its air filtration. Here, an adaptive electrospinning system based on reinforcement learning (E-RL) was developed to produce uniform-thickness nanofiber filters. The E-RL accomplished a real-time thickness measurement of an electrospun nanofiber filter by measuring the transmitted light through the nanofiber filter using a camera placed at the bottom of the collector and converting it into thickness using the Beer–Lambert law. Based on the measured thickness, the E-RL detected the non-uniformity of the nanofiber filter thickness and manipulated the movable collector to alleviate the non-uniformity of the thickness by a pre-trained reinforcement learning (RL) algorithm. For the training of the RL algorithm, the nanofiber production simulation software based on the empirical model of the deposition of the nanofiber filter was developed, and the training process of the RL algorithm was repeated until the optimal policy was achieved. After the training process with the simulation software, the trained model was transferred to the adaptive electrospinning system. By the movement of the collector under the optimal strategy of RL algorithm, the non-uniformity of such nanofiber filters was significantly reduced by approximately five times in standard deviation and error for both simulation and experiment. This finding has great potential in improving the reliability of electrospinning process and nanofiber filters used in research and industrial fields such as environment, energy, and biomedicine. Graphical Abstract