## 3 Results
### Results of Extrudability Tests of the Mycelium-Based Mixtures
Across the folds, the model achieved a low average error metrics--Mean Square Error (MSE): 0.301, Root Mean Squared Error (RMSE): 0.548, Mean Absolute Error (MAE): 0.423--and high degree of explained variance (R\({}^{2}\): 0.912) (although R\({}^{2}\) is a commonly reported metric, in our context it does not fully capture the nuances of model performance given the inherent non-linearities and variability in the data. Therefore, we place greater emphasis on error-based metrics such as MSE, RMSE, and MAE), which underscores the ANN's ability to capture the underlying complexities of mycelium-based extrusion processes while remaining relatively insensitive to the particular training subset (Figure 11). Following the cross-validation experiments, the final ANN model was evaluated on the hold-out test set, comparing its performance against two baseline continuous regression models: (1) Linear Regression and (2) third-degree Polynomial Regression. As presented in Figure 11, the final ANN model achieved superior performance across all error and explained variance metrics, which demonstrates its distinct advantage in modeling the non-linearities that arise in mycelium-based composites. These results suggest that simpler regressors do not fully capture the intricate interactions between robot speed, material de-gassing, and substrate heterogeneity, whereas the ANN excels precisely in these more complex regimes. Figure 10: Effect of reduced psyllium husk on the extrudability and buildability of different samples. Cylindrical samples (10 cm diameter, 8 layers, 8 mm bead width) after robotic EAM containing different amounts of psyllium husk ((**A**): 8%, (**B**): 6%, (**C**): 4%, (**D**): 2% of water weight). Figure 9: Mycelial colonization of the extruded ring-shaped sample over 7 days. Further validation of the ANN's performance is provided by our residual analysis. The predicted vs. actual plot (Figure 12a) reveals that the data points cluster tightly around the ideal diagonal line, demonstrating minimal systematic bias in the model's predictions. Moreover, the residual scatter plot (Figure 12b) shows that errors are evenly distributed around zero without any clear pattern or evidence of heteroscedasticity. This indicates that the ANN maintains consistent predictive accuracy across the entire range of extrusion speeds, reinforcing its robustness in capturing both global trends and local variations in the data. Collectively, these results validate the ANN as a reliable and powerful tool for optimizing robotic extrusion parameters in the fabrication of complex material systems, such as mycelium-based biocomposites. Figure 11: Performance comparison of different predictive models: Linear Regression, Polynomial Regression, and Artificial Neural Network (ANN). Figure 12: Validation of the ANN’s performance through residual analysis. (**a**) The predicted vs. actual plot shows data points clustering around the diagonal. (**b**) The residual plot demonstrates a balanced error distribution around zero. To achieve a target layer width of 8 mm and layer height of 5 mm, the model was used to predict the extrusion parameters (DO and RPM) corresponding to a range of robot arm velocities. The DO (digital output) here refers to the control signal to regulate the rotational speed of the extruder's stepper motor, ranging from 0 to 255. Table 6 presents these values. For all of the EAM experiments in this study, a robot arm velocity of 15 mm/s and a DO value of 38 are used, with the extrusion setting corresponding to approximately 15% of the full digital output range (0-255).