As extrusion-based bioprinting advances, notable challenges remain in printing parameter adjustment. Finding optimal values of key parameters such as speed, material volume and temperature requires extensive manual testing, consuming substantial material, resources and time. Moreover, manual experimentation does not always guarantee high-quality results. Although progress has been made in evaluating print quality, quantitative approaches have not yet yielded significant results. Current calibration methods focus on a specific material and geometry, making them invalid for other types of geometries or materials. To address this limitation, this study proposes a comprehensive multi-feature calibration framework designed to generate high-density, measurable data to be applicable to several hydrogel systems, pending material-specific validation, across a large spectrum of printable geometric features. The presented approach proposes a scalable pattern with basic, exhaustive geometric primitives, enabling deeper insight into the behavior of complex structures. Furthermore, redundant geometries in the pattern address repeatability problems caused by flow inconsistencies. This design allows the evaluation of key features of interest—such as line continuity, width, circularity, density —across different geometries. Additionally, this study proposes quantitative metrics for an objective selection of favourable parameter ranges tailored to specific features of interest. To evaluate the pattern, printing experiments have been conducted to illustrate how the resulting multi-feature data can be used to identify configurations that enhance structural quality. By integrating these metrics into data-driven computational approaches, this framework could significantly reduce experimentation time and resources, improve print quality, and increase reproducibility, thereby contributing to more efficient and precise solutions in the field of extrusion-based 3D bioprinting.
Díez et al. (2026) studied this question.