PrintMind — Structural Intelligence for 3D Printing

Overview

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.

Problem

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.

My Role

Owned the workflow concept, CAD-processing logic, severity-to-policy reasoning, slicer integration direction, technical documentation, and validation comparison for the internal test case.

Method

1. Import CAD model.
2. Define loading/support conditions.
3. Prepare geometry for analysis and slicing.
4. Run or integrate structural evaluation.
5. Convert predicted severity into print policy decisions.
6. Generate optimized slicing settings.
7. Compare optimized configuration against baseline print configuration.

Result

• Built a CAD-to-slicing workflow for structurally informed 3D printing.
• Integrated CAD processing, simulation-based severity evaluation, and slicer-level optimization logic.
• Implemented rule-based adaptive infill decision logic.
• Internal testing on one case showed up to 56% print-time reduction and 50% material reduction compared with the baseline print configuration.
• Established a foundation for future ML/surrogate and RL-based optimization workflows.

Engineering Value

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.

Limitations / Future Scope

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.

• Broader geometry testing
• FEA calibration and validation
• Surrogate model acceleration
• Conditional local modifier generation
• RL-based print policy optimization
• UI/reporting improvements for thesis and product-style workflow