Data-driven methods have shown to be effective in optimizing process parameters and predicting final properties of 3D printed polymer-based materials . Deneault et al. demonstrated the possibility of application of Bayesian optimization to optimize printing parameters autonomously. Li et al. showed that ensemble learning algorithms can accurately forecast the surface roughness of 3D-printed components in real-time through monitoring process parameters, and surface roughness measurements. Zhang et al. used data-driven predictive modeling approach to predict the tensile strength of the cooperative printed PLA samples by considering the effect of incline angle, the overlapping length, and the number of shells on the tensile strength. Sharma et al. build a model to predict the dimensional variation of 3D printed PLA and ABS specimens with different geometries using decision tree machine learning algorithm according to the effect of various printing parameters like wall thickness, infill density, build plate temperature, print speed, layer thickness, extrusion temperature.