Atomizer/README.md

# NX OptiMaster

> Advanced optimization platform for Siemens NX Simcenter with LLM-powered configuration

[![Python 3.10+](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/)
[![License](https://img.shields.io/badge/license-Proprietary-red.svg)](LICENSE)
[![Status](https://img.shields.io/badge/status-alpha-yellow.svg)](https://github.com)

## Overview

NX OptiMaster is a next-generation optimization framework for Siemens NX that combines:

- **LLM-Driven Configuration**: Use natural language to set up complex optimizations
- **Advanced Algorithms**: Optuna-powered TPE, Gaussian Process surrogates, multi-fidelity optimization
- **Real-Time Monitoring**: Interactive dashboards with live updates
- **Flexible Architecture**: Pluggable result extractors for any FEA analysis type
- **MCP Integration**: Extensible via Model Context Protocol

## Architecture

```
┌─────────────────────────────────────────────────────────┐
│                 UI Layer                                │
│  Web Dashboard (React) + LLM Chat Interface (MCP)       │
└─────────────────────────────────────────────────────────┘
                          ↕
┌─────────────────────────────────────────────────────────┐
│                 MCP Server                              │
│  - Model Discovery     - Config Builder                 │
│  - Optimizer Control   - Result Analyzer                │
└─────────────────────────────────────────────────────────┘
                          ↕
┌─────────────────────────────────────────────────────────┐
│           Execution Layer                               │
│  NX Core (NXOpen) + Optuna Engine + Custom Scripts      │
└─────────────────────────────────────────────────────────┘
```

## Quick Start

### Prerequisites

- **Siemens NX 2306+** with NX Nastran solver
- **Python 3.10+** (recommend Anaconda)
- **Node.js 18+** (for dashboard frontend)

### Installation

1. **Clone the repository**:
   ```bash
   git clone https://github.com/atomaste/nx-optimaster.git
   cd nx-optimaster
   ```

2. **Create Python environment**:
   ```bash
   conda create -n nx-optimaster python=3.10
   conda activate nx-optimaster
   ```

3. **Install dependencies**:
   ```bash
   pip install -e .
   # For development tools:
   pip install -e ".[dev]"
   # For MCP server:
   pip install -e ".[mcp]"
   ```

4. **Configure NX path** (edit `config/nx_config.json`):
   ```json
   {
     "nx_executable": "C:/Program Files/Siemens/NX2306/NXBIN/ugraf.exe",
     "python_env": "C:/Users/YourName/anaconda3/envs/nx-optimaster/python.exe"
   }
   ```

### Basic Usage

#### 1. Conversational Setup (via MCP)

```
You: My FEA is in C:\Projects\Bracket\analysis.sim, please import its features.

AI: I've analyzed your model:
    - Solution: Static Analysis (NX Nastran)
    - Expressions: wall_thickness (5mm), hole_diameter (10mm)
    - Mesh: 8234 nodes, 4521 elements

    Which parameters would you like to optimize?

You: Optimize wall_thickness and hole_diameter to minimize max stress while keeping mass low.

AI: Configuration created! Ready to start optimization with 100 iterations.
    Would you like to review the config or start now?

You: Start it!

AI: Optimization launched! 🚀
    Dashboard: http://localhost:8080/dashboard
```

#### 2. Manual Configuration (JSON)

Create `optimization_config.json`:

```json
{
  "design_variables": {
    "wall_thickness": {
      "low": 3.0,
      "high": 8.0,
      "enabled": true
    }
  },
  "objectives": {
    "metrics": {
      "max_stress": {
        "weight": 10,
        "target": 200,
        "extractor": "nastran_stress"
      }
    }
  },
  "nx_settings": {
    "sim_path": "C:/Projects/Bracket/analysis.sim",
    "solution_name": "Solution 1"
  }
}
```

Run optimization:
```bash
python -m optimization_engine.run_optimizer --config optimization_config.json
```

## Features

### ✨ Core Capabilities

- **Multi-Objective Optimization**: Weighted sum, Pareto front analysis
- **Smart Sampling**: TPE, Latin Hypercube, Gaussian Process surrogates
- **Result Extraction**: Nastran (OP2/F06), NX Mass Properties, custom parsers
- **Crash Recovery**: Automatic resume from interruptions
- **Parallel Evaluation**: Multi-core FEA solving (coming soon)

### 📊 Visualization

- **Real-time progress monitoring**
- **3D Pareto front plots** (Plotly)
- **Parameter importance charts**
- **Convergence history**
- **FEA result overlays**

### 🔧 Extensibility

- **Pluggable result extractors**: Add custom metrics easily
- **Custom post-processing scripts**: Python integration
- **MCP tools**: Extend via protocol
- **NXOpen API access**: Full NX automation

## Project Structure

```
nx-optimaster/
├── mcp_server/              # MCP server implementation
│   ├── tools/               # MCP tool definitions
│   ├── schemas/             # JSON schemas for validation
│   └── prompts/             # LLM system prompts
├── optimization_engine/     # Core optimization logic
│   ├── result_extractors/   # Pluggable metric extractors
│   ├── multi_optimizer.py   # Optuna integration
│   ├── config_loader.py     # Configuration parser
│   └── history_manager.py   # CSV/SQLite persistence
├── nx_journals/             # NXOpen Python scripts
│   ├── update_and_solve.py  # CAD update + solver
│   ├── post_process.py      # Result extraction
│   └── utils/               # Helper functions
├── dashboard/               # Web UI
│   ├── frontend/            # React app
│   └── backend/             # FastAPI server
├── tests/                   # Unit tests
├── examples/                # Example projects
└── docs/                    # Documentation

```

## Configuration Schema

See [docs/configuration.md](docs/configuration.md) for full schema documentation.

**Key sections**:
- `design_variables`: Parameters to optimize
- `objectives`: Metrics to minimize/maximize
- `nx_settings`: NX/FEA solver configuration
- `optimization`: Optuna sampler settings
- `post_processing`: Result extraction pipelines

## Development

### Running Tests

```bash
pytest
```

### Code Formatting

```bash
black .
ruff check .
```

### Building Documentation

```bash
cd docs
mkdocs build
```

## Roadmap

- [x] MCP server foundation
- [x] Basic optimization engine
- [ ] NXOpen integration
- [ ] Web dashboard
- [ ] Multi-fidelity optimization
- [ ] Parallel evaluations
- [ ] Sensitivity analysis tools
- [ ] Export to engineering reports

## Contributing

This is a private repository. Contact [contact@atomaste.com](mailto:contact@atomaste.com) for access.

## License

Proprietary - Atomaste © 2025

## Support

- **Documentation**: [docs/](docs/)
- **Examples**: [examples/](examples/)
- **Issues**: GitHub Issues (private repository)
- **Email**: support@atomaste.com

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