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Initial commit: NX OptiMaster project structure

Browse Source 
- Set up Python package structure with pyproject.toml
- Created MCP server, optimization engine, and NX journals modules
- Added configuration templates
- Implemented pluggable result extractor architecture
- Comprehensive README with architecture overview
- Project ready for GitHub push

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Anto01
2025-11-15 07:56:35 -05:00
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# Python
__pycache__/
*.py[cod]
*$py.class
*.so
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
.pytest_cache/
.coverage
htmlcov/
*.cover
.hypothesis/
# Virtual Environment
venv/
ENV/
env/
.venv
# IDEs
.vscode/
.idea/
*.swp
*.swo
*~
.DS_Store
# NX/FEA Files
*.op2
*.f06
*.f04
*.xdb
*.log
*.diag
*.pch
*.master
*.dball
*.ldra
*.sdb
*.sim.bak
*.prt.bak
# Optimization Results
optuna_study.db
optuna_study.db-journal
history.csv
history.bak
next.exp
RMS_log.csv
archives/
temp/
*.tmp
# Node modules (for dashboard)
node_modules/
.npm
.cache
dist/
build/
# Environment variables
.env
.env.local
# OS
Thumbs.db
desktop.ini

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Proprietary License
Copyright (c) 2025 Atomaste
All rights reserved.
This software and associated documentation files (the "Software") are the proprietary
property of Atomaste. Unauthorized copying, modification, distribution, or use of this
Software, via any medium, is strictly prohibited without prior written permission from
Atomaste.
For licensing inquiries, please contact: contact@atomaste.com

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# 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
---
**Built with ❤️ by Atomaste** | Powered by Optuna, NXOpen, and Claude

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{
"nx_executable": "C:/Program Files/Siemens/NX2306/NXBIN/ugraf.exe",
"python_env": "C:/Users/YourName/anaconda3/envs/nx-optimaster/python.exe",
"journals_dir": "./nx_journals",
"temp_dir": "./temp",
"default_timeout": 300,
"max_parallel_solves": 1
}

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{
"optimization": {
"max_iterations": 100,
"seed": 42,
"evaluate_baseline_first": true,
"sampler": {
"type": "TPE",
"n_startup_trials": 15,
"n_ei_candidates": 150,
"multivariate": true,
"gamma": 0.25,
"prior_weight": 1.0
}
},
"baseline": {
"parameter1": 10.0,
"parameter2": 20.0
},
"design_variables": {
"parameter1": {
"type": "float",
"low": 5.0,
"high": 15.0,
"description": "First design parameter",
"enabled": true
},
"parameter2": {
"type": "float",
"low": 10.0,
"high": 30.0,
"description": "Second design parameter",
"enabled": true
}
},
"objectives": {
"primary_strategy": "weighted_sum",
"direction": "minimize",
"metrics": {
"max_stress": {
"weight": 10,
"target": 200.0,
"description": "Maximum von Mises stress",
"units": "MPa",
"enabled": true,
"extractor": "nastran_stress",
"extractor_params": {
"subcase": 101,
"stress_type": "von_mises"
}
},
"mass": {
"weight": 1,
"target": 0.5,
"description": "Total mass",
"units": "kg",
"enabled": true,
"extractor": "nx_mass",
"extractor_params": {
"bodies": "all"
}
}
}
},
"nx_settings": {
"sim_path": "path/to/model.sim",
"solution_name": "Solution 1",
"post_solve_delay_s": 5,
"op2_timeout_s": 1800,
"op2_stable_s": 4
},
"post_processing": {
"archive_results": true,
"export_expressions": true,
"custom_scripts": []
}
}

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"""
NX OptiMaster MCP Server
Model Context Protocol server for LLM-driven NX optimization configuration.
"""
__version__ = "0.1.0"
__author__ = "Atomaste"

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"""
MCP Tools for NX OptiMaster
Available tools:
- discover_fea_model: Analyze .sim files to extract configurable elements
- build_optimization_config: Generate optimization config from LLM instructions
- start_optimization: Launch optimization run
- query_optimization_status: Get current iteration status
- extract_results: Parse FEA result files
- run_nx_journal: Execute NXOpen scripts
- search_nxopen_docs: Search NXOpen API documentation
"""
from typing import Dict, Any
__all__ = [
"discover_fea_model",
"build_optimization_config",
"start_optimization",
"query_optimization_status",
"extract_results",
"run_nx_journal",
]

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"""
NXOpen Journal Scripts
Python scripts that execute within the NX environment using NXOpen API.
These scripts are called via subprocess from the MCP server.
"""
__version__ = "0.1.0"

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"""
NX OptiMaster Optimization Engine
Core optimization logic with Optuna integration, reused and enhanced from Atomizer.
"""
__version__ = "0.1.0"

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"""
Pluggable Result Extractor System
Base classes and implementations for extracting metrics from FEA results.
"""
from abc import ABC, abstractmethod
from typing import Dict, Any, Optional
from pathlib import Path
class ResultExtractor(ABC):
"""Base class for all result extractors."""
@abstractmethod
def extract(self, result_files: Dict[str, Path], config: Dict[str, Any]) -> Dict[str, float]:
"""
Extract metrics from FEA results.
Args:
result_files: Dictionary mapping file types to paths (e.g., {'op2': Path(...), 'f06': Path(...)})
config: Extractor-specific configuration parameters
Returns:
Dictionary mapping metric names to values
"""
pass
@property
@abstractmethod
def required_files(self) -> list[str]:
"""List of required file types (e.g., ['op2'], ['f06'], etc.)."""
pass
@property
def name(self) -> str:
"""Extractor name for registration."""
return self.__class__.__name__.replace("Extractor", "").lower()
# Registry of available extractors
_EXTRACTOR_REGISTRY: Dict[str, type[ResultExtractor]] = {}
def register_extractor(extractor_class: type[ResultExtractor]) -> type[ResultExtractor]:
"""Decorator to register an extractor."""
_EXTRACTOR_REGISTRY[extractor_class().name] = extractor_class
return extractor_class
def get_extractor(name: str) -> Optional[type[ResultExtractor]]:
"""Get extractor class by name."""
return _EXTRACTOR_REGISTRY.get(name)
def list_extractors() -> list[str]:
"""List all registered extractor names."""
return list(_EXTRACTOR_REGISTRY.keys())
__all__ = [
"ResultExtractor",
"register_extractor",
"get_extractor",
"list_extractors",
]

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[project]
name = "nx-optimaster"
version = "0.1.0"
description = "Advanced optimization platform for Siemens NX Simcenter with MCP integration"
authors = [
{name = "Atomaste", email = "contact@atomaste.com"}
]
readme = "README.md"
requires-python = ">=3.10"
license = {text = "Proprietary"}
dependencies = [
"optuna>=3.5.0",
"pandas>=2.0.0",
"numpy>=1.24.0",
"scipy>=1.10.0",
"scikit-learn>=1.3.0",
"pyNastran>=1.4.0",
"plotly>=5.18.0",
"fastapi>=0.109.0",
"uvicorn>=0.27.0",
"websockets>=12.0",
"pydantic>=2.5.0",
"python-multipart>=0.0.6",
"jinja2>=3.1.3",
]
[project.optional-dependencies]
dev = [
"pytest>=7.4.0",
"pytest-cov>=4.1.0",
"black>=23.12.0",
"ruff>=0.1.0",
"mypy>=1.8.0",
"pre-commit>=3.6.0",
]
mcp = [
"mcp>=0.1.0",
]
dashboard = [
"dash>=2.14.0",
"dash-bootstrap-components>=1.5.0",
]
[build-system]
requires = ["setuptools>=68.0", "wheel"]
build-backend = "setuptools.build_meta"
[tool.setuptools.packages.find]
where = ["."]
include = ["mcp_server*", "optimization_engine*", "nx_journals*"]
[tool.black]
line-length = 100
target-version = ['py310']
include = '\.pyi?$'
[tool.ruff]
line-length = 100
select = ["E", "F", "I", "N", "W"]
ignore = ["E501"]
[tool.mypy]
python_version = "3.10"
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = false
[tool.pytest.ini_options]
testpaths = ["tests"]
python_files = ["test_*.py"]
python_classes = ["Test*"]
python_functions = ["test_*"]
addopts = "-v --cov=mcp_server --cov=optimization_engine --cov-report=html --cov-report=term"

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# Core Dependencies
optuna>=3.5.0
pandas>=2.0.0
numpy>=1.24.0
scipy>=1.10.0
scikit-learn>=1.3.0
pyNastran>=1.4.0
plotly>=5.18.0
# Web Framework
fastapi>=0.109.0
uvicorn>=0.27.0
websockets>=12.0
pydantic>=2.5.0
python-multipart>=0.0.6
jinja2>=3.1.3
# Development Tools (optional)
pytest>=7.4.0
pytest-cov>=4.1.0
black>=23.12.0
ruff>=0.1.0
mypy>=1.8.0