A simulation-guided CAD-to-slicing workflow for adaptive infill and manufacturable print optimization.
PrintMind is a thesis-driven engineering software workflow developed to make 3D printing decisions more structurally informed. Instead of relying only on uniform slicing parameters, the workflow connects CAD processing, loading condition definition, simulation-based severity evaluation, and slicer-level print parameter optimization.
Conventional FDM slicing workflows usually apply uniform infill and print settings across a part. This can lead to unnecessary material use, longer print times, or weak regions when the part is mechanically loaded. A more engineering-aware workflow should consider where loads act, how severe the part response is, and how print settings can be adapted while remaining manufacturable.
PrintMind was developed as a CAD-to-slicing engineering pipeline. The workflow processes CAD input, defines load/support conditions, evaluates structural severity using simulation-informed logic, and generates optimized print settings. The system uses rule-based decision logic as the current optimization layer, with the architecture designed to support future surrogate models, calibration, and reinforcement learning.
PrintMind demonstrates how mechanical design, simulation, and manufacturing workflow automation can be connected to create smarter 3D printing decisions. The project is not only a slicer modification; it is an engineering decision workflow that links structural demand with manufacturable print strategy.
The reported reduction values are based on an internal test case and should be presented as case-specific outcomes, not universal performance claims. Further validation across multiple geometries, load cases, and physical tests would be required for stronger generalization.