## 3 Results and Discussion
### Optimization of Process Parameters
#### 3.3.2 Confirmation Test and Linear Regression  
Finally, once the optimal levels of parameters were determined, Taguchi DoE confirmation tests were conducted at the optimal settings to compare the experimental results with the DoE predicted values. According to Table 10, C.I. with % 95 confidence (z = 1.96) is 8.12. Based on the Taguchi DoE for the five primary printing parameters and their interactions and the defined levels, the S/N ratio for the expected printability at the optimum of the parameters is 38.98 (Table 11). After converting the S/N ratio to the predicted value of printability (80.70),  
Figure 9: (a) Response table for signal-to-noise ratios; (b) analysis of variance table; (c) Taguchi’s design main effects plot for SN ratios for the five primary variables of printability. the agreeable range of the result can be calculated as follows: expected printability = optimum printability \(\pm\) C.I. = (72.59, 88.20). Consequently, the printability test of the optimal parameters' combination was also performed to be compared with the expected value. The average of the results was 82.1%, and it was within the given limit (72.59, 88.20). By setting the four secondary printing speed parameters at their optimum levels that were defined through the second Taguchi array, the design of the printability of alginate-gelatin hydrogel reached the promising values of 92% and 85% for strand and pore-area printability, respectively. Once the optimal levels of parameters were determined using the Taguchi design of experiments (DoE), the next step was to confirm the results by conducting experiments at the optimal settings and comparing the results with the predicted values from the DoE. This step is commonly known as the confirmation experiment. Finally, in order to understand how the values of response vary when the parameters are adjusted, linear regression was employed to propose a possible relationship between the five most critical variables affecting printability. Based on this, a regression model (Figure 11) was created using the Minitab program in the current study to forecast printability within the chosen parameters and their levels. The high value (87.17%) of the adjusted R-squared indicates a strong fit, and it suggests that the model can account for a significant amount of variability in the data (Figure 11a). As the tip diameter is a categorical predictor, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Descriptive** & **N** & **Mean** & **StDev** & **SE Mean** & **95\% C.I. for \(\mu\)** \\ \cline{2-6}
**Statistics** & 27 & 33.11 & 21.56 & 4.15 & (24.58; 41.64) \\ \hline \hline \multicolumn{6}{l}{\(\mu\): mean of printability (\%).} \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Confidence interval for the mean of printability (95%). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Settings** & **Alginate Ratio** & **Temperature** & **Extrusion Speed** & **Nozzle Diameter** & **Layer Height** \\
**(\%)** & **(\({}^{\circ}\)C)** & **(mm/s)** & **(mm)** & **(mm)** \\ \hline optimal levels & 8 & 24 & 2 & 0.41 & 0.25 \\ \hline \hline \multicolumn{6}{l}{\(\mu\): mean of printability (\%).} \\ \hline \hline \end{tabular}
\end{table} 
Table 11: [PERSON]’s DoE predicted value for optimum printability and confirmation test results. Figure 10: (**a**) Response table for signal-to-noise ratios; (**b**) analysis of variance table; (**c**) [PERSON]’s design main effects plot for SN ratios for the four secondary speed-related variables of printability. the model consists of three equations (Figure 11b), one for each of the categorical predictor's values. Each equation relates the other four continuous predictors with the response. In addition, as presented in the normal probability plot for the total of the responses (Figure 12a), as the line is not significantly skewed either left or right, we can assume that there is no significant deviation from the normal distribution for the observations.