Embedded printing is a highly promising approach for creating complex structures within a yield-stress support bath. However, the accurate prediction and control of printability remain fundamental challenges due to the complex interactions between inks and support baths. Here, we present an artificial intelligence (AI)-driven framework that interprets and predicts embedded printability using rheological data. Using a standardized workflow, we extracted 21 rheological descriptors and established 12 indicators to evaluate structural continuity and geometric fidelity. Interpretable machine learning models revealed that direction-dependent defects are governed by the synergistic interplay among ink yield stress, support bath zero-shear viscosity, flow behavior index, and time constant. To enable the prediction of printability in a generalizable manner, we further developed a cascaded neural network, which achieved mean relative prediction errors below 15% across all indicators. Experimental validation using three-dimensional (3D)-printed constructs and micro-computed tomography (μCT) reconstructions confirmed a strong correlation between predicted and actual fidelity. This work establishes a physics-informed, data-driven paradigm for decoding and optimizing embedded printing, offering broad applicability and providing a robust tool for the rapid pairing of suitable printable ink-support bath combinations.
Zhou et al. (Thu,) studied this question.