## 4 Results
### Optimal Parameter Settings for Multiple Performance Measurements  
As seen in the above results, the optimal parameter settings are varied depending on the performance measurement that is considered as an objective to be achieved for the additive manufacturing process. Thus, the determination of the optimal parameter settings for the FFF process of CFR-PEEK can be a multi-objective decision-making problem; trade-offs exist between the different performance measurements that are affected by the FFF process parameters. For example, the individual optimal setting results show that the 0.3 mm layer thickness minimizes printing time, but this layer thickness cannot achieve the minimized dimensional accuracy and material cost.  
Table 12 shows the optimal performance settings under the multiple response optimization among the printing time, dimensional accuracy, and material cost based on the fitted regression models only with the significant factors in each design case. For all the design cases, the parameter combination of 0.2 mm in layer thickness, 0\({}^{\circ}\) in build orientation, and 1400 mm/min in printing speed maximizes the composite desirability among all the parameter combinations. The individual desirability less than 0.7 in Table 12 indicates that the optimal settings are less effective to the performance measurement; the optimal parameter settings involve trade-offs between the responses. The lowest individual desirability in the printing time is consistently observed in each design case, given the optimal parameter settings, although the dimensional accuracy and material cost have a relatively higher desirability at the optimal parameter settings regardless of the design cases. Since the equal importance of the responses was assumed to obtain the optimal settings in Table 12, it can be inferred that the current optimal settings compromise time reduction to improve the dimensional accuracy and material cost when the responses are equally important. Thus, the optimal settings can be varied if more importance is assigned to layer thickness. For example, the current optimal settings are very effective to individually minimize dimensional error and material cost for the ASTM D638 design (\(d>0.9\)). However, the 0.3 mm layer thickness can be selected as an optimal setting to increase the desirability of printing time if the printing time has a much higher importance than other responses. Figure 11 shows the printed samples with the optimal parameter settings obtained from the multiple response optimization.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline \multirow{2}{*}{**Design**} & \multicolumn{2}{c}{**Optimal Parameter Settings**} & \multicolumn{2}{c}{**Desirability (\(d\))**} & \multirow{2}{*}{**Composite Desirability (\(D\))**} \\ \cline{2-2} \cline{5-6}  & **Layer Thickness** & **Build Orientation** & **Printing Speed** & **Printing Time** & **Dimensional Accuracy** & **Material Cost** \\ \hline ASTM D638 & 0.2 mm & 0\({}^{\circ}\) & 1400 mm/min & 0.71 & 0.99 & 0.92 & 0.87 \\ ASTM D695 & 0.2 mm & 0\({}^{\circ}\) & 1400 mm/min & 0.62 & 0.74 & 1.00 & 0.77 \\ ASTM D3039 & 0.2 mm & 0\({}^{\circ}\) & 1400 mm/min & 0.78 & 0.80 & 0.90 & 0.83 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Optimal parameter settings derived by multiple response optimization.  
\begin{table}
\begin{tabular}{l c c} \hline \hline \multicolumn{1}{c}{**(b) Regression Model**} \\ \hline \multicolumn{1}{c}{**Sample**} & \multicolumn{1}{c}{**Prediction Model**} & \multicolumn{1}{c}{**R-Sq.**} \\ \hline ASTM & y = 2.61 - 0.121L1 + 0.1212 - 0.0201 + 0.02202 & 99.95\% \\ DG38 & y = 8.40 - 0.04L1 + 0.04L2 - 0.1401 + 0.1402 & 94.58\% \\ ASTM D3039 & y = 5.34 - 0.15L1 + 0.15L2 - 0.1501 + 0.1502 & 92.56\% \\ \hline \hline \end{tabular}  
*: optimal value.  
\end{table}
Table 11: _Cont._