## 4 Conclusions and Future Perspectives  
As a vast majority of bioprinters employ 3D extrusion technology, the material formulation strategy should be improved in order to be completely in line with the selected AM  
Figure 11: (**a**) Statistical summary for the regression model for the five primary parameters affecting printability; (**b**) equations of the regression model for each value of tip diameter. Figure 12: (**a**) Normal probability plot for the total of the responses; (**b**) normal probability plot for grouped responses according to tip diameter value. technology. More specifically, biomaterial formulations should be examined in terms of processability in order to save time and reduce costs. The thorough study of the correlation between material properties and processability will enable the effective development of compliant feedstock material. At present, some studies have focused on the optimization of some 3D printing process parameters, but most of them have not focused on the bioprinting of scaffolds. [PERSON] et al., for instance, performed PCA in an attempt to classify the printing behavior of potato pastes. Their research findings refer only to food extrusion-based 3D printing and cannot predict the printability of other biomaterials [35]. A robust study of printability assessment through rheological evaluation of biomaterials was suggested by [PERSON] et al., who investigated the impact of various rheological properties while determining a range of values for critical printing settings. The findings, although promising, lack statistical quantification that could indicate the highly correlated rheological factors. Moreover, a study of the viscoelastic properties of the material could also add value to the optimization of ink property design. This approach could be improved by decreasing the number of printing trials by a DoE that takes into consideration the significance of each printing parameter [36]. Regarding enhancing property ink design, more types of commonly used materials, such as hydrogels, should be further investigated in order to decrease the number of printing trials necessary to choose the optimal material for a certain application. In this scope, investigation of AM requirements in terms of materials properties based on DIW-specific criteria will, such as feedstock composition and rheological properties, are to be tackled. Recently, the training of machine learning (ML) algorithms in bioprinting has attracted significant attention. Enriching databases and creating digital twins of human cells/tissues/organs with the appropriate properties are among the future challenges [37]. The objective of this study was not to present the well-established alginate-gelatin hydrogel but to systematically investigate the printability aspects of this specific hydrogel blend and provide insights for its optimization. Particularly, the present work studied alginate-gelatin hydrogel blend's printing accuracy while aiming to determine the key rheological parameters highly correlated with the material's printability and the printing settings that optimized the development process. Regarding processability in AM technologies, ink property design, as well as process optimization, were realized. PCA was used to analyze the link between material properties and printability, whereas the two-step Taguchi DoE method was utilized to optimize printing parameters for an extrusion-based 3D printer to achieve the desired printing accuracy. More specifically, PCA revealed that viscosity, m, G\({}^{\prime}\), and G\({}^{\prime\prime}\) govern blends' printing behavior, and based on the scores of the candidate inks, the printability window for each rheological property was defined. The results of Taguchi's robust design showed that the optimum set of the parameters is 8%, 24\({}^{\circ}\), 2 mm/s, 0.41 mm, 0.58 mm, 3 mm/s, 2 mm/s, 30 mm/s, and 50 mm/s for alginate ratio, head temperature, extrusion speed, nozzle diameter, layer height, perimeter speed, infill speed, retract speed, and travel speed, respectively.