For example, a non-Newtonian behavior characterized by significant shear thinning has been identified as crucial for successful printing, as it ensures stable extrusion and sufficient melt strength in a semi-solid state, while extreme viscoelastic properties have been shown to prevent printing . Furthermore, the rheological property flow index (n) has proven useful in adjusting the printing speed to achieve a consistent volumetric flow rate . Moreover, minimized melting enthalpy or specific volume change has been shown to prevent geometrical instability, such as warpage, while printing polymer-based materials . Considering the impact of process factors and material properties on 3D printing and final product quality, printing process optimization of PNCs requires a significant amount of trial-and-error, which results in a significant amount of material and time waste, rendering traditional experimental methods costly. As a result, it is crucial to predict if the newly designed PNCs can be 3D printed successfully while capturing the intricate relationship between material properties and printability.