Multi-parametric numerical analysis of 3D printed sparse infill structures

Due to the increase in 3D printing popularity, optimizing the procedure has become an important aspect of fused deposition modeling (FDM) research. As such, a numerical framework that captures the effect of all parameters involved in the process can aid in manufacturing optimization by overcoming the cost and time limitations of experimental methods. In this work, a numerical physics-based model is created that takes as input process parameters, simulates the phenomenon, and identifies the temperature history and developed mechanical stresses during printing. Initially, this model is validated through images taken from an infrared camera. Then, a design of experiments (DOE) sequence that covers the range of each input variable is created and multiple designs are evaluated. Correlations are examined and factor analysis on each output is performed indicating that material and bed temperature are the most important parameters affecting mechanical stresses, while fan speed dominates cooling. The simulation data are fed into data-driven models to provide accurate estimations on cooling duration, plastic strain, and von Misses stress and in turn aid in optimal parameter selection. This work improves existing numerical approaches by incorporating multiple process parameters and aids in digital twinning of FDM.