## 1 Introduction
To expedite mycelium growth, lignocellulosic substrates or nutrient supplements are added to the paste as fibers. Hence, both a gelling agent and lignocellulosic substrate are combined for MBC additive manufacturing [17]. Fiber size range and shape greatly influence the flow and packing behavior of dense pastes for EAM. These factors affect both the paste flow during EAM and the stability of the final structure [21]. At ForMat Lab in collaboration with mycologists from the Plant Pathology department at Penn State, we have developed extrudable mixtures and workflows for EAM of living mycelium-based composites [16]. The comprehensive workflow of EAM can be delineated into six steps, as outlined by [12; 13]: mycelium inoculation, primary colonization, mixing, EAM, secondary colonization, and drying. It is important to mention that these steps have the potential to be combined or reordered depending on the application. In this research, the workflow was streamlined by establishing a new workflow for preparing living, extrudable mycelium mixtures, involving alterations in the preparation sequence and adjustments in the admixture ratios. The resultant mycelium mixture was employed in a comprehensive series of experiments to optimize the parameters of robotic EAM. These parameters include layer height, extrusion speed, and robot arm velocity. An Artificial Neural Network (ANN) model was used, in which the resulting layer height and width data are used as inputs. This model was used to accurately determine the specific printing parameters required to achieve a predefined layer height and width, thereby ensuring precise control over the robotic EAM process. This approach allows for a more refined and data-driven method to fine-tune the EAM parameters. As the next step, a detailed experimental framework is established, utilizing the Taguchi method to generate nine varied acoustic samples [22]. This design of experiments (DOE) focuses on altering key parameters, including infill patterns, the relative proportions of layers with distinct patterns, and the infill percentage. The objective is to systematically assess the impact of these variables on acoustic absorption. For the empirical evaluation, an impedance tube was used to measure the normal incidence sound absorption coefficients at various frequencies for each sample. This method provides a quantitative analysis of how the different robotic EAM parameters affect the acoustic absorption characteristics across a range of frequencies. Lastly, conformal toolpath generation and robotic EAM of biodegradable acoustic wall panels from living mycelium mixtures were explored based on previous research by the authors [23]. While prior studies focused on paste formulation and extrusion feasibility of MBCs [12; 13; 16], this study advances both the paste formulation and the EAM workflow. The paste is developed using ground Ecovative's Dehydrated Grow-It-Yourself Mushroom(r) Material (Ecovative Design LLC, Green Island, NY, USA), with inoculation initiated prior to EAM. This eliminates the need for a separate colonization step in growth bags, enabling direct EAM of the living material and significantly reducing the total fabrication timeline. Additionally, this study uses a data-driven strategy using an Artificial Neural Network (ANN) for optimizing the robotic EAM parameters for mycelium-based mixtures, which can be extended to future formulations for consistent and predictable extrusion. A hybrid slicing approach combining planar and non-planar slicing methods improves surface quality and enables fabrication of conformal geometries. Finally, this study uses robotic EAM for the fabrication of architectural-scale acoustic panels, demonstrating its potential for fabrication of performance-based building parts beyond conventional mold-based methods. This study underscores the transformative capacity of robotic EAM and conformal toolpath design utilizing mycelium-based biomaterials, presenting the development of sustainable biodegradable building materials and advanced acoustic solutions.