## 2 Materials and Methods
### Parameter Optimization of Robotic EAM Processes  
The use of mycelium mixtures in EAM for fabricating biocomposites introduces unique challenges due to their material properties. Unlike more homogeneous materials such as clay, mycelium composites exhibit a degree of variability that complicates the consistency of extrusion rates. This variability is not solely due to the material's heterogeneity but also a result of its tendency to de-gas, a process where air trapped within the composite is released during material deposition. This de-gassing effect can cause significant changes in the volume of the material as air is released, which in turn affects the stability of the extrusion rate--a factor critical to achieving accurate deposition of material. Given the complexity of predicting the mycelium mixture's behavior under extrusion, a data-driven approach utilizing machine learning offers a potential solution. By framing the task as a regression problem, we aim to establish a predictive model that can infer optimal extrusion speeds from a given set of robotic EAM parameters, thereby enhancing the precision and reliability of the robotic EAM process with mycelium-based materials. To achieve this goal, one of the key steps was collecting a comprehensive dataset that accurately reflected the behavior of mycelium mixtures during the robotic EAM process. Initially, heuristic approaches were employed to delineate an experimental space, setting baseline parameters for fabrication processes. This preliminary step was crucial to construct a foundational understanding of the relationship between robot speed and extrusion rate. From this, a preliminary Linear Regression model was developed to predict extrusion rates from robot speeds. This model guided the experimental design across a spectrum of robot speeds, facilitating the collection of empirical data. For each speed, the process was run at variations of \(\pm\)50% around the predicted extrusion rate. During these runs, a single continuous bead of material was deposited, and the resulting layer height and width were \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mixture** & \begin{tabular}{c} **Ground Ecovative** \\ **Mixture** \\ **(GM)** \\ \end{tabular} & \begin{tabular}{c} **Water** \\ **(W)** \\ \end{tabular} & \
\begin{tabular}{c} **Psyllium Husk** \\ **(PH)** \\ \end{tabular} &
\begin{tabular}{c} **Flour** \\ **(F)** \\ \end{tabular} \\ \hline A & 100 g & 600 mL & 48 g & 16 g \\ B & 100 g & 600 mL & 36 g & 16 g \\ C & 100 g & 600 mL & 24 g & 16 g \\ D & 100 g & 600 mL & 12 g & 16 g \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different mixtures to assess the effect of reducing psyllium husk on the extrudability and buildability of the formulations. meticulously measured with a caliper every 10 mm. This procedure was repeated across 42 experimental runs, each with 12 replicate measurements, yielding a comprehensive dataset reflective of the robotic EAM process's nuances. The collected data were further processed into new features, namely difference in layer height (\(\Delta h_{layer}=h_{observed}-h_{slicer}\)), and difference in layer width (\(\Delta w_{layer}=w_{observed}-d_{nozzle}\)), aiming to provide a clearer signal for the model to learn from as compared to raw measurements. In our experimental observations, significant under- and over-extrusion at the extreme ends of the robot speed range indicated that the relationship between the robotic EAM parameters and extrusion speed was not strictly linear. To capture these interactions, we adopted an Artificial Neural Network (ANN) approach--well suited for modeling non-linear relationships and generalizing from data where traditional linear models fall short.