An integrated computer vision system for real-time monitoring and control of long-fiber embedded hydrogel 3D printing

Abstract

3D printed hydrogel components have been used in a wide range of applications including tissue engineering and soft and biohybrid robotics. Advances in embedded printing, such as the Freeform Reversible Embedding of Suspended Hydrogels (FRESH), have further improved the geometric fidelity of 3D printed hydrogels using a fugitive support bath. Recently, it has been shown that the structural rigidity of hydrogels fabricated with embedded 3D printing can be significantly reinforced for a wider range of applications using the Long-fiber Embedded FRESH (LFE-FRESH) technique, an extension of FRESH by combing fiber embedding and hydrogel 3D printing. However, fibers are prone to buckling under compression load due to the high slenderness ratio and maintaining fiber stability during embedding is vital for LFE-FRESH. In this study, we introduce an integrated computer vision (CV) system for the continuous monitoring and control of LFE-FRESH, which actively adjusts the fiber embedding process in real-time by quantifying fiber deformation from the video data and controlling the fiber extrusion motor. Using the prototype, we demonstrated that the integrated CV system effectively prevents fiber buckling, corrects for over-extrusion during the LFE-FRESH process, and improves fiber embedding quality. Moreover, this technique was implemented with low-cost, mass-produced components and can be conveniently integrated with existing open-source LFE-FRESH software and hardware, which improves its accessibility and facilitates future adaptations for new research applications.