| description abstract | Due to the nature of additive manufacturing (AM), design and manufacturing are deeply coupled. Toolpaths are defined based on the part geometry, and in turn, these toolpaths can influence the bonding between adjacent toolpaths, especially for fused filament fabrication (FFF) process. In FFF, bonding between adjacent rasters is critical to the FFF part mechanical strength. The bonding is driven by factors such as thermal history and a deposition strategy, which are dictated by the geometry of a part and process parameters. In this research, a data-driven physics-based methodology is proposed to predict the mechanical properties of FFF parts using Bayesian inference. In the proposed methodology, geometry and variance in process parameters are used to quantify uncertainties in the mechanical properties. Empirical data derived from the mesostructure of specimens are utilized to generate priors of predictors. Hamilton Monte Carlo is then used to sample the posterior distribution. Subsequently, random draw from posterior predictive distribution is performed, and the results are validated against empirical data to establish the accuracy of the proposed methodology. The proposed methodology can provide more accurate prediction of the mechanical properties by considering the influence of geometry, process parameters and uncertainty in AM process. | |
