Measuring part geometric accuracy is typically performed with various approaches, including ultrasonic, X-ray tomography, and laser scanning [8–12]. The resulting data are typically analyzed using statistical models (e.g., response surface regression and analysis of variance [13–16]) to develop an empirical mapping of the relationship between input process parameters and geometric accuracy. For example, Wang et al. [17] used least squares regression to correlate shrinkage of fabricated parts and process parameters (e.g., laser power, layer pitch, and scanning speed) in the stereolithography (SLA) process. Zhou et al. [13] used a 3D coordinate measuring machine and surface profilometer to capture geometric accuracy, and surface roughness of parts in SLA process. Through Taguchi experimental designs, Zhou et al. [13] found that by adjusting process parameters, such as layer thickness, hatch spacing, overcure, blade gap, and position on build plane, the part errors can be controlled to be less than 0.0013 mm/mm. Furthermore, Noriega et al. [18] used an artificial neural network model coupled with an optimization algorithm to improve GD&T characteristics such as distance between parallel faces in FFF fabricated parts. However, these previous works only use limited number of samples with simple geometries to model GD&T characteristics such as cylindricity and flatness; thus, these works cannot capture the critical features for the parts with complex shape [19,20].