## 4 Results and Discussion
### Investigation of the Inputs' Influence on the Responses
Considering the molded counterparts, a study on polyamide milling [50] revealed roughness values as low as approximately 1.2 \(\upmu\)m, suggesting that conventionally manufactured polymers tend to produce higher-quality surfaces. Especially during micromachining of conventionally fabricated nanocomposites [51], the produced roughness falls under the range of below 1 \(\upmu\)m.  
Additionally, machining 3D-printed polymers, particularly those produced via FFF, presents distinct challenges compared to machining conventionally manufactured polymers such as extruded or injection-molded materials. The layer-by-layer construction inherent to FFF introduces anisotropic mechanical properties and surface irregularities, which can lead to increased tool wear and inconsistent surface finishes during machining. For instance, a study comparing the milling of 3D-printed and cast polyamide [50] revealed that FFF parts exhibited lower surface quality and required specific machining parameters to achieve acceptable finishes, whereas cast parts demonstrated more uniform machinability. Similarly, research on 3D printed thermoplastics milling [52] points out the importance of effective heat dissipation through proper chip formation to maintain high surface quality during machining. Furthermore, an analysis of milling parameters on PLA, PETG, and CF-PETG [23] highlighted that 3D-printed materials respond differently to changes in feed rate, spindle speed, and depth of cut, emphasizing the need for tailored machining strategies to optimize surface quality. These findings underscore the importance of understanding the unique characteristics of 3D-printed polymers to effectively machine them for real-time applications. To visualize the significance of each one of the input parameters and their interactions, a sensitivity analysis was performed. Figure 6(a) illustrates the plot for \(Ra\) and Figure 6(b) for \(T\). In addition, these plots summarize, in a way, the findings of Figure 6. The bars depict the normalized sensitivity of each input parameter and their interactions on the predicted surface roughness, as determined by the trained ANFIS model. The longer the bar, the more influential the factor. By observing Figure 6(a), it is evident that the most influential factor is the interaction between feed rate and depth-of-cut (\(f\times ap\)), indicating that these two parameters jointly have a dominant impact on \(Ra\). Interaction between cutting speed and feed rate (\(Vc\times f\)) shows high significance as well, followed by the individual feed rate (\(f\)). Moderate influence is observed from the interaction between cutting speed and depth-of-cut (\(Vc\times ap\)). Finally, the individual effects of cutting speed (\(Vc\)) and depth-of-cut (\(ap\)) are relatively weaker in comparison. These insights suggest that combined parameter effects are more significant than individual ones, especially when optimizing for surface quality. The model highlights the importance of carefully tuning feed rate and depth-of-cut, both individually and jointly, during the machining of the specific material. Similarly, Figure 6(b) visualizes the interaction between cutting speed and feed rate (\(Vc\times f\)), which was identified to be the dominant factor, accounting for nearly 30% of the sensitivity. This indicates a strong synergistic effect between these two parameters on heat generation at the tool tip. The cutting speed (\(Vc\)) alone also shows a high influence, affirming its direct relationship with heat generation due to higher shearing rates and friction at elevated speeds. The interaction \(Vc\times ap\) contributes similarly, suggesting that cutting speed combined with a higher material removal rate (via depth) significantly impacts temperature. Feed rate (\(f\)) and the interaction \(f\times ap\) have moderate influence, reflecting their roles in cutting load and chip thickness, which also affect thermal behavior.