Reverse CAD Generation and Evaluation for 3D-Printed Part Performance with Diverse Design Parameters

Additive manufacturing is increasingly used for functional design, where optimizing print parameters is essential for achieving desired mechanical performance while minimizing time and material usage. However, the large number of printing parameters with varied effects on mechanical properties makes design challenging. This study investigates the influence of infill density and infill pattern by creating an integrated experimental and computational approach with systematic design characterization. CAD models were generated from G-code using reverse modeling to represent the internal structure of printed parts, and tensile tests were simulated to evaluate stiffness with physical validation. This reverse CAD generation is automated to extract flow velocity from G-code and calculate the extruded filament shape, with no manual input. Parts with a line infill pattern oriented parallel to the loading direction exhibited the highest elastic modulus of 2346 MPa at 100% infill density, as well as the highest stiffness per unit printing time (59.6 MPa/s) and per unit mass. The lowest modulus (359 MPa) was observed in line-patterned parts with a 0° raster orientation at 50% infill density. The results demonstrate that the experimentally validated computational workflow can generate and evaluate large datasets of mechanically evaluated parts across print parameters, thereby supporting efficient design when many parameters require consideration.