## 3 Results and Discussion
### Productivity  
Looking at the prediction plot and regression model for productivity, Figure 15 and Equation (7), the importance of layer thickness and printing speed is clearly visible, which matches with the results for the energy consumption, and suggests a strong dependency on build time. The importance of these two parameters and their interaction is verified by ANOVA, where the three most important terms were found to be: first, layer thickness (\(p\)-value = \(1.10\times 10^{-24}\)), second, printing speed (\(p\)-value = \(8.52\times 10^{-24}\)), and third, interaction of layer thickness and printing speed (\(p\)-value = \(8.88\times 10^{-9}\)). This is enhanced by the aspect that greater layer thickness and faster printing speed both increase material consumption per unit time. \[ \begin{split} Productivity&=0.223+0.011*x_{1}+0.101*x_{2}+0.9 6*x_{3}-0.005*x_{4}-0.004*x_{5}+\\ 0.004*x_{1}x_{2}+0.007*x_{1}x_{3}+0.003*x_{1}x_{4}+0.043*x_{2}x_{3}-0.008* x_{2}x_{5}+0.003*x_{3}x_{4}-\\ 0.001*x_{3}x_{5}+0.001*x_{4}x_{5}-0.001*x_{2}^{2}-0.026*x_{3}^{2}+0.003*x _{5}{}^{2}\end{split}  \tag{7}\]  
where \"\(x_{1}\)-\" is the normalized value for infill percentage, \"\(x_{2}\)\" refers to the normalized value of layer thickness, \"\(x_{3}\)\" denotes the normalized value of printing speed, \"\(x_{4}\)\" is the normalized value for printing temperature, and \"\(x_{5}\)\" represents the normalized value for the surface inclination angle. Higher infill percentage requires greater material consumption, also a longer build time, so infill percentage was, as expected, linearly related to productivity, but was not an important factor as layer  
Figure 14: Effects of interaction of parameters on measured energy consumption. Figure 15: Predicted effects of changes in process parameters on productivity. thickness and printing speed. In addition, noticeable is the slight decrease in productivity when increasing the printing temperature, whereas it might be expected that higher printing temperature produces a more liquid filament, which should facilitate the flow and thus enhance productivity. Figure 16 shows the interaction of parameters regarding productivity. The interaction plots of layer thickness and printing speed both show clearly that the effect of one parameter is reinforced by a high value of the other, which can be seen in the growing gap between plots and the low \(p\)-value in ANOVA. Similar to the interactions regarding energy consumption, we once again note that the influence of printing temperature is only visible at low speeds and layer thicknesses, and appears as a separation between the plots, e.g., see at 0.1 mm layer thickness and 20 mm/s printing speed. By Equation (1), it can be said that productivity can mainly be increased by reducing the build time independently from the model mass, although a higher mass can enhance this effect. Hence, the analysis of production time in both [PERSON] et al. [32] and [PERSON] et al. [27] can be used for comparison. Their results agree with those obtained in this study, confirming the high impact of printing speed and layer thickness on build time and therefore, productivity. Furthermore, [PERSON] et al. [31] investigated the model material volume, which is proportional to the mass. It was found that the model volume rises with a higher contour width, thus a more solid part. This goes in hand with this study's finding that a higher infill percentage, hence a denser part, enhances productivity. Since productivity is the only response that should be maximized, it was possible to use the outer boundary values for each parameter: 50% infill, 0.3 mm layer thickness, 100 mm/s printing speed, and a printing temperature of 230 \({}^{\circ}\)C. According to the prediction plot, the temperature should be set as low as possible to increase productivity, but because of the interaction of printing speed and printing temperature, a high temperature is advisable when using a printing speed of 100 mm/s.