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Electrohydrodynamic printing (EHDP) is able to precisely manipulate the position, size, and morphology of micro-/nano-fibers and fabricate high-resolution scaffolds using viscous biopolymer solutions. However, less attention has been paid to the influence of EHDP jet characteristics and key process parameters on deposited fiber patterns. To ensure the printing quality, it is very necessary to establish the relationship between the cone shapes and the stability of scaffold fabrication process. In this work, we used a digital microscopic imaging technique to monitor EHDP cones during printing, with subsequent image processing algorithms to extract related features, and a recognition algorithm to determine the suitability of Taylor cones for EHDP scaffold fabrication. Based on the experimental data, it has been concluded that the images of EHDP cone modes and the extracted features (centroid, jet diameter) are affected by their process parameters such as nozzle-substrate distance, the applied voltage, and stage moving speed. A convolutional neural network is then developed to classify these EHDP cone modes with the consideration of training time consumption and testing accuracy. A control algorithm will be developed to regulate the process parameters at the next stage for effective scaffold fabrication.

Electrohydrodynamic printing process monitoring by microscopic image identification