## 2 Materials and Methods
### Parameter Optimization of Robotic EAM Processes
Our ANN, implemented using the Keras API for TensorFlow, features a 3-32-32-1 architecture: three input features (robot speed, \(\Delta h_{layer}\), and \(\Delta w_{layer}\)) are processed through two hidden layers of 32 neurons each using the Exponential Linear Unit (ELU) activation function, and the network culminates in a single output neuron that predicts the extrusion speed. Prior to training, input features were standardized using a RobustScaler, which enhances resilience against outliers and ensures consistent scaling of the key parameters influencing extrusion quality. The dataset was partitioned into training and test sets using an 80-20 split. Model training was first validated via 5-fold cross-validation conducted exclusively on the training data, with each fold undergoing independent scaling to prevent data leakage. Subsequently, the final model was trained on the complete training set and evaluated on the hold-out test set. During cross-validation, early stopping was employed to mitigate overfitting, and a learning rate reduction on the plateau ensured smooth convergence. The Adam optimizer was selected for its robustness and efficiency in handling the complexities of noisy, heterogeneous data. Our training objective was to minimize the mean squared error (MSE) between predicted and actual extrusion speeds, thereby fine-tuning the network to capture the intricate balance of factors inherent in the robotic EAM process. Performance-Based Design and Conformal Tolpath Development for Robotic Extrusion-Based Additive Manufacturing of Acoustic Wall Panels  
The design of the proof-of-concept acoustic wall is informed by simulations in Pachyderm, an open-source acoustic analysis plugin for Rhinoceros 3D (sourced from [[https://www.food4](https://www.food4) rhino.com/en/app/pachyderm-acoustical-simulation]([https://www.food4](https://www.food4) rhino.com/en/app/pachyderm-acoustical-simulation) (accessed on 6 May 2025)) [29]. Pachyderm employs raytracing and image-source methods to predict room acoustics, using geometrical features and surface material data as inputs. The normal incidence absorption coefficients of the mycelium-based composites are included as inputs in these simulations. The primary objective is to validate mycelium-based composites as noise control materials and inform the design of the acoustic absorber wall before the robotic fabrication of panels. To compare the parametric design alternatives for the mycelium-based acoustic absorber wall, reverberation time is chosen as the comparative parameter in Pachyderm. Reverberation time refers to the amount of time it takes for the sound pressure level to reduce 60 dB after the sound source stops producing sound [30]. For our purposes, shorter reverberation times are preferred, as longer reverberation times are associated with reduced speech intelligibility and quality [31]. The simulation environment consists of an empty room, a geodesic sound source, a stationary receiver, and the proposed mycelium-based acoustic wall. Sound absorption coefficients are assigned to the interior surfaces of the 3D model using Pachyderm's material editor. Each simulation traces 5000 rays to obtain the room's reverberation time and is run four times. The results are averaged to reduce error. This study employs a geometric design space for methodically exploring the effects of surface complexity on acoustic performance, aiming to establish parametric control over the geometric feature frequency. This was achieved by deriving an interference pattern between a 2D simplex noise and a 1D sine wave across the proposed design. In our implementation, the parametric model was defined by three variables. The first was the scale of the 2D simplex noise, responsible for dictating the overall complexity of the design's surface. The second variable was the frequency of the 1D sine wave, controlling the pattern periodicity within the design. Lastly, the amplitude of the resulting interference pattern was set, allowing us to adjust the depth and intricacy of the design.