Hydrogels have become pivotal materials for tissue engineering, robotics, biomedical devices, and sensing applications due to their diverse material compositions and tunable mechanical properties. While significant effort has focused on developing novel manufacturing approaches such as extrusion bioprinting and light-based fabrication methods, there has been limited work in real-time characterisation of manufactured parts, which often requires tedious parameter optimization to achieve the desired structural resolution and material properties. Here, we demonstrate a high-throughput approach based on Dynamic Interface Printing (DIP) that enables simultaneous in situ fabrication, mechanical characterisation, and volumetric quantification of centimeter-scale hydrogel scaffolds within seconds. We establish automated stiffness-seeking capabilities through a characterisation-in-the-loop framework employing zero-order optimization algorithms, achieving target elastic moduli within 3%-5% accuracy across diverse material formulations without prior knowledge of material properties or structural information. We further introduce a three-dimensional nodal framework for volumetric grayscale lithography that generates spatially heterogeneous mechanical properties, demonstrating engineered nonlinear stress-strain relationships through crosslinking density modulation. In addition, we implement orthogonal light-sheet illumination coupled with machine learning segmentation algorithms, enabling real-time layer-wise structural reconstruction with >85% accuracy. This integrated methodology eliminates manual handling, automating part design and significantly shortening optimization timelines by providing real-time quantitative feedback on morphology and mechanical properties.
Vidler et al. (2026) studied this question.