Effect of slicing parameters on the as-manufactured state of 3D printed parts utilizing numerical modeling

The optimization of fused deposition modeling (FDM) is crucial for advancing additive manufacturing by enhancing both the production process and the quality of printed parts. 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. This paper presents a novel numerical framework that contributes to this goal by combining mesoscale and macroscale simulations, enabling improved predictions of mechanical properties, reduced printing time, and minimized material usage. By capturing the thermal history and bond formation in FDM, the proposed mesoscale approach simulates the manufacturing process of tensile and bending specimens. These simulations are then scaled up using representative volume element (RVE) homogenization to predict macroscale bending behavior. The model is validated through close agreement with experimental data, generating a comprehensive dataset for 10 slicing parameters. The simulation outcomes are further utilized in data-driven models to predict key outputs-apparent Young's modulus, filament usage, printing time, and ultimate tensile strength (UTS). The current work provides a basis and a repeatable end-to-end methodology for optimal parameter selection in FDM.