3D bioprinting technologies have the potential to revolutionize biomedical material production. However, scaling from laboratory prototypes to industrial production presents challenges in maintaining material consistency and reproducibility. To address these challenges, we introduce a computational framework to enable the digital twinning of the co-axial bioprinting process. We integrate physics-based modeling, data-driven model-order reduction, and feedback control. Co-axial printing employs a dual-nozzle system for the simultaneous extrusion of a hydrogel precursor and a crosslinking agent, enabling the fabrication of 3D structures with precisely controlled mechanical properties [1]. A significant challenge in this method is ensuring consistent material properties across large-scale batches and achieving reproducibility and uniformity in the final products. In this work, we introduce a framework comprising a high-fidelity multiphysics model of the diffusion-reaction dynamics during printing [2,3] and a feedback control strategy to maintain a desired Degree of Crosslinking (DoC), which is directly linked to the hydrogel's mechanical properties. Controlling the DoC allows for tailoring hydrogels to specific applications [4]. A data-driven reduced-order model (ROM) is identified to optimally tune the feedback controller parameters and enable real-time simulation. Numerical experiments demonstrate that the proposed controller can guide the printing process to the desired DoC with acceptable performance, and it remains stable under different operating condition adjustments. This work aims to advance the additive manufacturing of functional materials by leveraging computational methods for digital twinning. The ultimate goal is a closed-loop system that consistently yields high-quality, functional hydrogel products, advancing the reliability of bioprinting for industrial-scale applications such as fabricating human tissue analogs. REFERENCES [1] Kjar, A., McFarland, B., Mecham, K., Harward, N., & Huang, Y. (2021). Engineering of tissue constructs using coaxial bioprinting. Bioactive materials, 6(2), 460-471. [2] Urrea-Quintero, J. H., Marino, M., Wick, T., & Nackenhorst, U. (2024). A comparative analysis of transient finite-strain coupled diffusion-deformation theories for hydrogels. Archives of Computational Methods in Engineering, 1-34. [3] Agarwal, G., Urrea-Quintero, J. H., Wessels, H., & Wick, T. (2024). Parameter identification and uncertainty