*3.2. Build Time*

The ANOVA table revealed that Infill IP and IP2 of the printer were the most significant controlled variables for the build time. Table 5 demonstrates the ANOVA analysis for the build time. Equations (3) and (4) represent the final regression equation based on the coded values and actual values for the build time:

$$\begin{aligned} \text{(Build Time)}^{-3} &= 2.24976 \times 10^{-5} - 1.20444 \times 10^{-5} \text{ IP} - 2.51081 \times 10^{-7} \text{ ET} \\ &+ 3.00044 \times 10^{-5} \text{ IP}^2 - 1.27876 \times 10^{-5} \text{ ET}^2 \end{aligned} \tag{3}$$

$$\begin{aligned} \text{(Build Time)}^{-3} &= -0.000548983 - 5.85298 \times 10^{-6} \text{ IP} + 6.24335 \times 10^{-6} \text{ ET} \\ &+ 7.50109 \times 10^{-8} \text{ IP}^2 - 1.42085 \times 10^{-8} \text{ ET}^2 \end{aligned} \tag{4}$$

Regression equations' terms had superb advantages in this study because many reasons, such as coded equation, could provide a suitable perception to physical parameters. Here, in the build time, LT, LP, and ET had a significant effect on the 3D printed samples. Due to Table 5, it was clear that LT was not very effective than either parameter and had a steady change. Results showed that when the IP rose, the built time increased. Also, when the ET rose, the built time reduced (Figure 8c) too. The probability plot in Figure 9 showed the residuals to illustrate the normality of residuals. In this diagram, the trend of the normal distribution in some particular samples was applied in a direct line. When the normal distribution became stable, the model was suitable for the build time, and it was clear that the normal distribution was close to the direct line.


**Table 5.** Analysis of variance (ANOVA).

**Figure 8.** 3D surface plot of the build time with (**a**) infill percentage and layer thickness; (**b**) extruder temperature and layer thickness; (**c**) infill percentage and extruder temperature.

**Figure 9.** The normal plot of residuals of the build time.

#### *3.3. Thickness*

Table 6 depicts the ANOVA output and input parameters outcome for one of the important and significant features of samples. It could be found that LT and ET were the most effective variables. Part thickness' predictive model in terms of coded factors and actual amounts are represented in Equations (5) and (6), respectively.

$$(\text{Thickness})^{0.86} = 340.4628 + 97.46882 \text{ LT} - 5.40253 \text{ IP} - 58.3206 \text{ ET} - 210.432 \text{ IP} \times \text{ET} \tag{5}$$

$$\text{(Thickness)}^{0.86} = -3285.51 + 4990.265 \,\text{LT} + 64.80622 \,\text{IP} + 15.92595 \,\text{ET} - 0.35072 \,\text{IP} \times \text{ET} \tag{6}$$


**Table 6.** Analysis of variance (ANOVA).

The excellent R-squared and adjusted R-squared of the predictive model confirmed that the model was immensely reliable. As shown in Figure 10a, by raising the infill percentage, the amount of the thickness increased. Figure 10b revealed that with raising the layer thickness and the extruder temperature, the thickness increased. The reason for this phenomenon was that when ET and LT grew up, the material printed rose. That is because the LT always equated with more material injection. Therefore, the amount of thickness increased. Figures 11 and 12 show the perturbation plot and normal plot of residuals of the thickness, respectively.

**Figure 10.** 3D surface plot of the maximum width with (**a**) infill percentage and layer thickness; (**b**) extruder temperature and layer thickness; (**c**) infill percentage and extruder temperature.

**Figure 11.** Perturbation plot of the maximum width.

**Figure 12.** The normal plot of residuals of thickness.

#### **4. Numerical Optimization**

In this study, for the sake of numerical optimization, three criteria were evaluated. Three criteria of these experiments are shown in Table 7. Table 8 shows the predicted optimum results and experimental validation for Br-PLA 3D printing samples. Some parameters of physical and mechanical properties were considered as output parameters because it is essential for manufacturing samples with good conditions, such as proper resist from tensile strengths and adequate thickness. The optimization method provided an efficient condition to produce these samples. As a matter of fact, the suitable portion of each material was very important in the composite structure. Br-PLA consisted of two phases with a ratio of 35% to 65%, wherein the variation of the 3D printing input parameters played an important role in producing samples without any defects. The strong samples with the least deformation were the main goal of this article. Based on Table 8, the predicted optimum results and experimental validation were very close together and showed slight errors between them. Overly diagram in Figure 13 illustrates two parts of optimization in which substantial region in input parameters was relevant by output parameters. It means that the variation of each parameter had a significant role in output results. Also, in Figure 14, the results for the higher tensile strength in optimum samples are shown. In a previous study [18], in the PLA 3D printing samples, the maximum failure load was reported more than Br-PLA samples because the composite structure had the more particle's space, while, in Br-PLA, the metal component took up more space than PLA structure. Therefore, the PLA parts had more resistance in the tensile strength test.

**Table 7.** Constraints and criteria of input parameters and responses.



**Table 8.** Predicted optimum results and experimental validation.

**Figure 13.** Overlay plot of 3D printing optimization with (**a**) infill percentage and extruder temperature; (**b**) extruder temperature and layer thickness.

**Figure 14.** Extension-force diagram of the specimen for solution 3.

#### **5. Comparison of PLA and Br-PLA 3D Printed Samples**

In this part, the comparison of PLA and Br-PLA 3D printed samples were investigated. From Section 4, which is related to the extension-force result of the specimen for solution 3 of Br-PLA composite and also PLA optimum sample in the previous study [18], respectively, it was clear that the tensile strength of PLA was higher than the Br-PLA composite. This phenomenon happened because of two reasons. Firstly, when the Br-PLA composite parts were printed, the infill percentage was less than PLA printed parts in the constant situation and input parameters. The second reason was that PLA is a single material and has the good connection between its particles, whereas, in the Br-PLA sample, because two materials are used, the connection of particles are weaker than PLA sample, but the flexibility of the Br-PLA part is higher than the PLA part [36].

#### **6. Conclusions**

FDM 3D printing method for producing the Br-PLA samples was improved by the DOE approach and considering the significant input parameters (infill percentage, extruder temperature, and layer thickness) for each output parameter (maximum failure load, build time, and sample thickness). In the continuation of the article, some of the conclusions are mentioned:


**Author Contributions:** Conceptualization, M.M.; methodology, M.M. and M.K.M.; software, M.K.M. and M.S.; validation, M.M., M.K.M., and M.B.; investigation, M.M., M.K.M., and M.B.; resources, M.M.; writing—original draft preparation, M.M., M.K.M., and M.B.; writing—review and editing, M.M., M.K.M., M.S., and M.B.; supervision, M.M.; project administration, M.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Conflicts of Interest:** The authors declare no conflict of interest.
