Anto01
8efa8ba0d1
Major improvements to NX import workflow and rib pattern generation:
**NX Import (import_profile.py)**
- Smart sketch management: detects existing sketches and updates in-place
- Preserves extrude references (no manual re-reference needed!)
- First run: creates new sketch + auto-extrude
- Subsequent runs: clears geometry, redraws, extrude regenerates automatically
- Added _find_sketch_by_name() and _clear_sketch_geometry() functions
**Rib Pattern Variations**
Generated 3 different density field strategies for testing NX updates:
- Balanced (α=1.0, β=0.3): Original moderate density - 86 pockets, 2,499g
- Edge-focused (α=0.3, β=1.5): Dense ribs near boundaries - 167 pockets, 2,328g
- Hole-focused (α=1.8, β=0.15): Dense around holes - 62 pockets, 3,025g
**New Files**
- import_profile_update_test.py: Standalone update-only test script
- params_large_triangles.json: s_min=30mm, s_max=100mm (larger triangles)
- params_edge_focused.json: β=1.5 (boundary reinforcement)
- params_hole_focused.json: α=1.8 (hole reinforcement)
- sandbox_results/{edge_focused,hole_focused}/: Complete rib profile sets
**Test Results (Sandbox 1)**
- 833 triangles with large triangle params (vs 1,501 with previous params)
- Edge-focused: 1,155 triangles, 167 pockets (2x denser)
- Hole-focused: 523 triangles, 62 pockets (sparse pattern)
This enables rapid rib pattern iteration in NX without losing extrude references!
Co-Authored-By: Claude Sonnet 4.5 <noreply@anthropic.com>
Adaptive Isogrid — Plate Lightweighting Tool
Status: Foundation / Pre-Implementation
Architecture: Python Brain + NX Hands + Atomizer Manager
What It Does
Takes a plate with holes → generates an optimally lightweighted isogrid pattern → produces manufacturing-ready geometry. Isogrid density varies across the plate based on hole importance, edge proximity, and optimization-driven meta-parameters.
Architecture
| Component | Role | Runtime |
|---|---|---|
| Python Brain | Density field → Gmsh Frontal-Delaunay → rib profile | ~1-2 sec |
| NX Hands | Import profile → mesh → AFEM merge → Nastran solve → extract results | ~60-90 sec |
| Atomizer Manager | Optuna TPE sampling → objective evaluation → convergence | 500-2000 trials |
Key Insight: Assembly FEM with Superposed Models
- Model A (permanent): Spider elements at holes + edge BC nodes. All loads/BCs applied here.
- Model B (variable): 2D shell mesh of ribbed plate. Rebuilt each iteration.
- Node merge at fixed interface locations connects them reliably every time.
Loads and BCs never need re-association. Only the rib pattern changes.
Directory Structure
adaptive-isogrid/
├── README.md
├── requirements.txt
├── docs/
│ └── technical-spec.md # Full architecture spec
├── src/
│ ├── brain/ # Python geometry generator
│ │ ├── __init__.py
│ │ ├── density_field.py # η(x) evaluation
│ │ ├── triangulation_gmsh.py # Gmsh Frontal-Delaunay meshing (production)
│ │ ├── pocket_profiles.py # Pocket inset + filleting
│ │ ├── profile_assembly.py # Final plate - pockets - holes
│ │ └── validation.py # Manufacturing constraint checks
│ ├── nx/ # NXOpen journal scripts
│ │ ├── extract_geometry.py # One-time: face → geometry.json
│ │ ├── build_interface_model.py # One-time: Model A + spiders
│ │ └── iteration_solve.py # Per-trial: rebuild Model B + solve
│ └── atomizer_study.py # Atomizer/Optuna integration
└── tests/
└── test_geometries/ # Sample geometry.json files
Implementation Phases
- Python Brain standalone (1-2 weeks) — geometry generator with matplotlib viz
- NX extraction + AFEM setup (1-2 weeks) — one-time project setup scripts
- NX iteration script (1-2 weeks) — per-trial mesh/solve/extract loop
- Atomizer integration (1 week) — wire objective function + study management
- Validation + first real project (1-2 weeks) — production run on client plate
Quick Start (Phase 1)
cd tools/adaptive-isogrid
pip install -r requirements.txt
python -m src.brain --geometry tests/test_geometries/sample_bracket.json --params default
Parameter Space
15 continuous parameters optimized by Atomizer (Optuna TPE):
- Density field: η₀, α, R₀, κ, p, β, R_edge
- Spacing: s_min, s_max
- Rib thickness: t_min, t₀, γ
- Manufacturing: w_frame, r_f, d_keep
See docs/technical-spec.md for full formulation.