## 3 Results
### Results of Acoustic Absorption Testing  
To contextualize the acoustic behavior of the samples, Table 6 presents the sound absorption coefficients (\(\alpha\)) of all samples at standard octave band frequencies (125-4000 Hz). These values offer a preliminary characterization of the acoustic performance associated with different toolpath planning factors. However, as the Taguchi L9 design does not account for interaction effects of different toolpath planning factors, the results in Table 7 reflect the absorption coefficient of each sample and cannot be used to isolate the effect of individual factors. All fabricated samples (S1-S9) demonstrated an average sound absorption coefficient (\(\alpha\)) of more than 0.2, which classifies them as sound-absorbing materials. Notably, samples S1 (\(\alpha\) = 0.31), S6 (\(\alpha\) = 0.23), and S7 (\(\alpha\) = 0.29) showed the highest absorption coefficient, particularly in the high-frequency ranges (1000-4000 Hz) \begin{table}
\begin{tabular}{c c c c c} \hline
**Robot Arm Velocity (mm/s)** & **Predicted DO (0–255)** & **Predicted RPM** & **Layer Height (mm)** & **Layer Width (mm)** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 6: Predicted DO values and corresponding RPM by the ANN model for a range of robot arm velocities. The target layer height and width were fixed at 5 mm and 8 mm, respectively. The statistical analysis, conducted using Minitab (Web-Based Version. Minitab LLC: State College, PA, USA. [[https://app.minitab.com/](https://app.minitab.com/)]([https://app.minitab.com/](https://app.minitab.com/)) (accessed on 6 May 2025)) and using the Taguchi L9 array, provided valuable information on the impact of toolpath planning parameters on the acoustic performance of the fabricated cylindrical samples. The analysis was performed on two main metrics, mean performance and signal-to-noise ratios. Mean performance reflects the average absorption coefficient, while the SN ratio evaluates how consistently each factor level performs under varying conditions while minimizing the impact of noise factors (uncontrollable variations, such as material inconsistencies or environmental factors). The \"Larger-is-Better\" signal-to-noise (SN) ratio formulation was applied in this analysis, as maximizing the absorption coefficients corresponds to improved acoustic performance. For non-planar layers, a 60% infill percentage was identified as the most effective in both mean performance and SN ratios, particularly at high frequencies (1000-4000 Hz). In contrast, higher infill percentages of 75% and 90% demonstrated reduced performance, indicating that overly dense non-planar layers may not effectively dissipate sound. In planar layers, a clear upward trend in both mean performance and SN ratios was observed as the infill percentage increased from 60% to 90%. The 90% infill level significantly outperformed other levels, indicating that denser planar layers contribute positively to sound absorption. The analysis also highlighted that the Differential Growth Curve (DGC) infill pattern consistently outperformed the Parallel and 3D Honeycomb patterns across all metrics. This pattern proved effective in both planar and non-planar layers for acoustic application. Finally, the layer ratio of 2:6 (non-planar-planar) emerged as the most effective configuration, with the highest mean performance and SN ratio (Figure 13). The identified configurations, 60% infill for non-planar layers, 90% infill for planar layers, the DGC pattern, and a 2:6 layer ratio, provided a validated framework for determining toolpath planning parameters. These levels were selected as the foundation for slicing and generating the toolpath for the robotic EAM of the proof-of-concept acoustic panels.