Electrohydrodynamic printing process monitoring by microscopic image identification

Abstract

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.

Engineered Nanotopography on the Microfibers of 3D-Printed PCL Scaffolds to Modulate Cellular Responses and Establish an In Vitro Tumor Model

“…This observation is likely due to the charged amino acid residues in the peptide chains of gliadin that alter the electrical properties of the supplied composite inks. The charge could be released through parameter optimization, , making it feasible to stably print PCL/gliadin scaffolds.…”

“…A high-voltage power output from the power supply was applied between the nozzle and conductive substrate to generate an electric field required by the EHDP technique. Furthermore, a digital microscope (B011, Supereyes Technology Co., Ltd., China) was installed to monitor the real-time cone jet formation and printing processes as previously described …”

Machine intelligence for nerve conduit design and production

“…Optimize micro-droplet generator, electrohydrodynamic, drop-ondemand, and spheroid-based bioprinters [199][200][201]205] NGC Performance SWIRL for predicting graft success Bootstrap aggregated neural networks for predicting conduit performance [206][207][208] Abbreviations…”

“…Machine sensors can analyze acoustic signals or video recordings to assess the quality of printed layers by identifying aberrant features [203,204]. Machine vision can also fine-tune the electrohydrodynamic printing (EHDP) by monitoring cone shapes for more precise scaffold biofabrication [205]. Digital microscopic imaging combined with CNN Developing automated inspection systems using machine intelligence can reduce print variation experienced during conduit production.…”

Expanding Melt‐Based Electrohydrodynamic Printing of Highly‐Ordered Microfibrous Architectures to Cm‐Height Via In Situ Charge Neutralization

“…[4] A CCD camera was used to dynamically record the EHD printing process. [34] Structural Characterization: The organization of the EHD-printed microfibrous architectures was mainly characterized by a scanning electron microscope (SEM, S-3000N, Hitachi, Japan). The 3D profile of the printed scaffolds was visualized and reconstructed by using a micro-computerized topography (μCT, YXLON, German).…”