## 4 Discussion and Conclusions  
This study underscores the potential of MBCs in sustainable construction, particularly for acoustic applications. By leveraging robotic EAM, we developed acoustic panels that exhibit promising sound absorption properties. The proposed material formulation and robotic EAM process demonstrated that MBCs could serve as viable alternatives to synthetic acoustic insulators. The material formulation used in this study demonstrated significant improvements by reducing the EAM timeline presented in the literature [12; 13; 16], from 12 days to 7 days. This was achieved by using ground Ecovative mixture directly, instead of colonized material, to prepare the paste. The resulting formulation, with a weight ratio of 5:30:1.2:0.8 for ground Ecovative mixture, water, psyllium husk, and flour, allowed for a substantial 54% reduction in water content compared to previously developed mixtures based on the literature [12; 13; 16]. This reduction in the water content was critical to minimize the deformation and shrinkage of the samples after EAM and provide a better extrudability and extrusion through an 8 mm nozzle without clogging throughout the process. The formulation was further validated through sieve analysis, which revealed a particle size distribution (PSD) for consistent flow and material deposition across different test experiments. The majority of particles fell within the range of 850-1400 \(\upmu\)m, with D10, D50, and D90 values measured at 412 \(\upmu\)m, 1227 \(\upmu\)m, and 1833 \(\upmu\)m, respectively. These metrics are critical to achieving stable rheological properties and preventing the nozzle blockage during EAM, while establishing a repeatable and standardized metric for the particle size of the ground Ecovative mixture. The evaluation of the Artificial Neural Network (ANN) model underscores its predictive accuracy and practical applicability in optimizing the robotic EAM parameters for mycelium-based mixtures, achieving an R\({}^{2}\) of 0.912 with low error metrics (MSE = 0.301, RMSE = 0.548, MAE = 0.423). The ANN model demonstrated superior performance compared to Linear and Polynomial Regression, particularly in capturing the complex, non-linear relationships inherent to the EAM process. Residual analysis indicated minimal bias and uniform error  
Figure 15: Fabricated two adjacent acoustic panels (250 by 250 mm each) using an MBC paste composed of ground Ecovative’s Dehydrated Grow-It-Yourself Mushroom® Material, water, psyllium husk, and flour. The panels were fabricated by robotic EAM, growing at 25 \({}^{\circ}\)C and 65% humidity for 7 days, drying in an incubator oven at 90 \({}^{\circ}\)C for 24 h, and post-processing of the panels involved sanding the edges to have flush boundary conditions for seamless assembly. distribution, validating its reliability across varying conditions. These results highlight the ANN's effectiveness as a computational tool for enhancing precision and control in the robotic EAM of mycelium-based materials. The acoustic assessment revealed that toolpath planning parameters significantly influenced the sound absorption characteristics of the fabricated samples. Key findings demonstrated that a 60% infill for non-planar layers, 90% infill for planar layers, the Differential Growth Curve (DGC) pattern, and a 2:6 non-planar-to-planar layer ratio provided better acoustic performance. However, the scope of this study was limited by selected factors and levels tested within the experimental framework. While the Taguchi L9 orthogonal array provided valuable outcomes, it limited the exploration of potential interactions between factors and the comprehensiveness of the analysis. Future work should aim to address these limitations by considering a broader range of factors and levels, as well as using more extensive experimental arrays, to capture the complexities of toolpath planning and material behavior.