WP-02 Feature-Based Manufacturability Inference
| Field |
Value |
| Priority |
High |
| Status |
Framing |
| Problem Space |
DFM and manufacturing reasoning |
| Linked Capability |
manufacturing physics, feature recognition, explainable review logic |
| Best Collaboration Shape |
applied thesis + pilot + rules study |
| Owner |
unassigned |
What This Work Package Is
Map CAD and drawing features to manufacturability-relevant reasoning that can later support review comments, rule suggestions, and DFM guidance.
Why It Matters To RapidDraft
- Directly supports the DFM product story
- Connects geometry understanding to production-aware output
- Can produce explainable logic rather than only black-box predictions
Expected Deliverables
- candidate feature schema
- manufacturability risk taxonomy
- first rule or inference prototype
- validation on a narrow manufacturing family
Candidate Lanes
| Lane |
People or Group |
Why They Matter |
Best First Ask |
Priority |
| FAU KBE and design-methods lane |
Sandro Wartzack |
clearest public signal for KBE, engineering drawings, tolerancing, and decision reuse |
advisor call on feature schema and explainable DFM framing |
High |
| TUM CCBE AI4CADCAM |
Dr. Stavros Nousias, André Borrmann |
strong CAD feature recognition and downstream CAD/CAM reasoning signal |
thesis or mini-project around feature recognition for packaging machinery parts |
High |
| Hochschule Kempten IDF |
Frank Schirmeier and Ulrich Göhner |
direct public signal on graph neural networks on CAD structures and industrial GenAI |
applied mini-project tied to SOMIC or SME pilot framing |
High |
| TU Darmstadt PLCM |
Benjamin Schleich |
strong engineering automation and PLM-informed product reasoning lane |
later-stage thesis or grant-framing discussion |
Medium |
Recommended First Opportunity
Ideal Partner Profile
- applied product-development or manufacturing-methods strength
- comfort with explainable engineering logic
- ability to work on a narrow domain rather than generic "AI for manufacturing"
Candidate Evidence Types
- feature-based DFM work
- engineering knowledge capture
- process-aware CAD or product development research
Recommended Next Actions
- Pick one first manufacturing family before opening broad outreach.
- Use FAU first for schema and framing, then Kempten or TUM CCBE for applied implementation depending on the chosen vertical.
- Keep the first prototype focused on explainable risk logic, not full autonomous DFM.
- Treat Kempten as the strongest packaging-aware applied path once the SOMIC-specific angle is ready.
Open Questions
- Which process family gives the best first collaboration wedge?
- How should this package connect to standards-as-data work later?
Sources
TextCAD/04_Marketing and Outreach/13_Universities/deep-research-report Balanced.md
TextCAD/04_Marketing and Outreach/13_Universities/deep-research-report Monster.md
TextCAD/04_Marketing and Outreach/13_Universities/deep-research-report.md