# 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

1. **Python Brain standalone** (1-2 weeks) — geometry generator with matplotlib viz
2. **NX extraction + AFEM setup** (1-2 weeks) — one-time project setup scripts
3. **NX iteration script** (1-2 weeks) — per-trial mesh/solve/extract loop
4. **Atomizer integration** (1 week) — wire objective function + study management
5. **Validation + first real project** (1-2 weeks) — production run on client plate

## Quick Start (Phase 1)

```bash
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.