3D bioprinting enables rapid fabrication of complex biological structures for tissue engineering applications. However, optimizing bioink formulation remains challenging due to complex relationships among material properties, printability, and cell viability. While the perimeter ratio (Pr) is commonly used to evaluate printability, it cannot adequately capture the full geometric fidelity required for comprehensive printability assessments, thereby limiting robust bioink design. To address this limitation, a novel Hausdorff distance (HD) metric is employed to quantify printability, directly measuring the maximum deviation between the designed and printed structures. Furthermore, multiple machine-learning approaches were applied to alginate-hyaluronic acid composite inks and rat pheochromocytoma-derived PC12 cells to assess printability and cell viability. Rheological parameters were characterized using support vector regression (SVR) with R ² ⩾ 0.974. Multi-layer perceptron (MLP) regressors achieved R ² values of 0.932 and 0.945 when predicting HD values of printed grid structures and cell viability, respectively. A regression-based convolutional neural network (CNN) was developed to predict HD values directly from grid structure images, achieving an R ² of 0.986. Through optimization, optimal as-extruded cell viability (⩾95%) was achieved while maintaining high printability (HD ⩽ 0.20). The optimal ink composition was further demonstrated with good long-term cell viability and proliferation potential. This proposed AI-integrated approach can dramatically reduce ink optimization time by rapidly predicting rheological properties, printability, and cell viability from minimal experimental data.

Three-dimensional (3D) bioprinting has emerged as a promising technology in the field of tissue engineering. Notably, the advancement of multi-ink printing technology is crucial for further progress in 3D bioprinting. In this study, we developed a single-nozzle system with multiple inlets for multi-ink bioprinting that achieves high switching efficiency through a combination of numerical and experimental approaches. This single-nozzle system demonstrates the potential for higher-resolution printing and quicker ink switching compared with multi-nozzle printing systems. In general, inks used in bioprinting have low viscosity (<10 Pa・s); however, their behaviors inside a single nozzle have not been thoroughly investigated. Initially, we conducted numerical simulations to analyze fluid behavior within single nozzles, focusing on the junction of multiple ink inlets, to propose an advanced nozzle design. We proposed a novel index, Se, for evaluating the switching behavior of the bioink inside the single nozzle. Numerical simulation results showed that the nozzle design and combinations of inks affected Se. In addition, subsequent experimental analysis confirmed the consistency of the simulation results. The proposed design, developed using simulations, featured a single nozzle with enhanced switching efficiency, demonstrating a smaller transition length compared with that of conventional single nozzles or T-junction nozzles in printing line structures of different viscous inks. This is the first study to employ numerical simulation in designing a single nozzle with multiple inlets to switch ink in multi-ink bioprinting. This methodology will broaden the potential of single nozzles for high-resolution printing in bioprinting applications.