Atomizer/README.md

Atomizer

Advanced LLM-native optimization platform for Siemens NX Simcenter

Overview

Atomizer is an LLM-native optimization framework for Siemens NX Simcenter that transforms how engineers interact with optimization workflows. Instead of manual JSON configuration and scripting, Atomizer uses AI as a collaborative engineering assistant.

Core Philosophy

Atomizer enables engineers to:

• Describe optimizations in natural language instead of writing configuration files
• Generate custom analysis functions on-the-fly (RSS metrics, weighted objectives, constraints)
• Get intelligent recommendations based on optimization results and surrogate models
• Generate comprehensive reports with AI-written insights and visualizations
• Extend the framework autonomously through LLM-driven code generation

Key Features

• LLM-Driven Workflow: Natural language study creation, configuration, and analysis
• Advanced Optimization: Optuna-powered TPE, Gaussian Process surrogates, multi-objective Pareto fronts
• Dynamic Code Generation: AI writes custom Python functions and NX journal scripts during optimization
• Intelligent Decision Support: Surrogate quality assessment, sensitivity analysis, engineering recommendations
• Real-Time Monitoring: Interactive web dashboard with live progress tracking
• Extensible Architecture: Plugin system with hooks for pre/post mesh, solve, and extraction phases
• Self-Improving: Feature registry that learns from user workflows and expands capabilities

📘 See DEVELOPMENT_ROADMAP.md for the complete vision and implementation plan.

Architecture

┌─────────────────────────────────────────────────────────┐
│                 LLM Interface Layer                     │
│  Claude Skill + Natural Language Parser + Workflow Mgr  │
└─────────────────────────────────────────────────────────┘
                          ↕
┌─────────────────────────────────────────────────────────┐
│              Optimization Engine Core                   │
│  Plugin System + Feature Registry + Code Generator      │
└─────────────────────────────────────────────────────────┘
                          ↕
┌─────────────────────────────────────────────────────────┐
│           Execution Layer                               │
│  NX Solver (via Journals) + Optuna + Result Extractors  │
└─────────────────────────────────────────────────────────┘
                          ↕
┌─────────────────────────────────────────────────────────┐
│              Analysis & Reporting                       │
│  Surrogate Quality + Sensitivity + Report Generator     │
└─────────────────────────────────────────────────────────┘

Quick Start

Prerequisites

• Siemens NX 2412 with NX Nastran solver
• Python 3.10+ (recommend Anaconda)
• Git for version control

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/Atomizer.git
   cd Atomizer
   

2. Create Python environment:

   conda create -n atomizer python=3.10
   conda activate atomizer
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Configure NX path (edit if needed):

   • Default NX path: C:\Program Files\Siemens\Simcenter3D_2412\NXBIN\run_journal.exe
   • Update in optimization_engine/nx_solver.py if different

Basic Usage

Example 1: Natural Language Optimization (Future - Phase 2)

User: "Let's create a new study to minimize stress on my bracket"

LLM: "Study created! Please drop your .sim file into the study folder,
     then I'll explore it to find available design parameters."

User: "Done. I want to vary wall_thickness between 3-8mm"

LLM: "Perfect! I've configured:
     - Objective: Minimize max von Mises stress
     - Design variable: wall_thickness (3.0 - 8.0 mm)
     - Sampler: TPE with 50 trials

     Ready to start?"

User: "Yes, go!"

LLM: "Optimization running! View progress at http://localhost:8080"

Example 2: Current JSON Configuration

Create studies/my_study/config.json:

{
  "sim_file": "studies/bracket_stress_minimization/model/Bracket_sim1.sim",
  "design_variables": [
    {
      "name": "wall_thickness",
      "expression_name": "wall_thickness",
      "min": 3.0,
      "max": 8.0,
      "units": "mm"
    }
  ],
  "objectives": [
    {
      "name": "max_stress",
      "extractor": "stress_extractor",
      "metric": "max_von_mises",
      "direction": "minimize",
      "weight": 1.0,
      "units": "MPa"
    }
  ],
  "optimization_settings": {
    "n_trials": 50,
    "sampler": "TPE",
    "n_startup_trials": 20
  }
}

Run optimization:

python tests/test_journal_optimization.py
# Or use the quick 5-trial test:
python run_5trial_test.py

Features

• Intelligent Optimization: Optuna-powered TPE sampler with multi-objective support
• NX Integration: Seamless journal-based control of Siemens NX Simcenter
• Smart Logging: Detailed per-trial logs + high-level optimization progress tracking
• Plugin System: Extensible hooks at pre-solve, post-solve, and post-extraction points
• Study Management: Isolated study folders with automatic result organization
• Resume Capability: Interrupt and resume optimizations without data loss
• Web Dashboard: Real-time monitoring and configuration UI
• Example Study: Bracket stress minimization with full documentation

🚀 What's Next: Natural language optimization configuration via LLM interface (Phase 2)

For detailed development status and todos, see DEVELOPMENT.md. For the long-term vision, see DEVELOPMENT_ROADMAP.md.

Project Structure

Atomizer/
├── optimization_engine/        # Core optimization logic
│   ├── runner.py              # Main optimization runner
│   ├── nx_solver.py           # NX journal execution
│   ├── nx_updater.py          # NX model parameter updates
│   ├── result_extractors/     # OP2/F06 parsers
│   │   └── extractors.py      # Stress, displacement extractors
│   └── plugins/               # Plugin system (Phase 1 ✅)
│       ├── hook_manager.py    # Hook registration & execution
│       ├── pre_solve/         # Pre-solve lifecycle hooks
│       │   ├── detailed_logger.py
│       │   └── optimization_logger.py
│       ├── post_solve/        # Post-solve lifecycle hooks
│       │   └── log_solve_complete.py
│       └── post_extraction/   # Post-extraction lifecycle hooks
│           ├── log_results.py
│           └── optimization_logger_results.py
├── dashboard/                  # Web UI
│   ├── api/                   # Flask backend
│   ├── frontend/              # HTML/CSS/JS
│   └── scripts/               # NX expression extraction
├── studies/                    # Optimization studies
│   ├── README.md              # Comprehensive studies guide
│   └── bracket_stress_minimization/  # Example study
│       ├── README.md          # Study documentation
│       ├── model/             # FEA model files (.prt, .sim, .fem)
│       ├── optimization_config_stress_displacement.json
│       └── optimization_results/  # Generated results (gitignored)
│           ├── optimization.log    # High-level progress log
│           ├── trial_logs/         # Detailed per-trial logs
│           ├── history.json        # Complete optimization history
│           └── study_*.db          # Optuna database
├── tests/                      # Unit and integration tests
│   ├── test_hooks_with_bracket.py
│   ├── run_5trial_test.py
│   └── test_journal_optimization.py
├── docs/                       # Documentation
├── atomizer_paths.py          # Intelligent path resolution
├── DEVELOPMENT_ROADMAP.md      # Future vision and phases
└── README.md                  # This file

Example: Bracket Stress Minimization

A complete working example is in studies/bracket_stress_minimization/:

# Run the bracket optimization (50 trials, TPE sampler)
python tests/test_journal_optimization.py

# View results
python dashboard/start_dashboard.py
# Open http://localhost:8080 in browser

What it does:

1. Loads Bracket_sim1.sim with wall thickness = 5mm
2. Varies thickness from 3-8mm over 50 trials
3. Runs FEA solve for each trial
4. Extracts max stress and displacement from OP2
5. Finds optimal thickness that minimizes stress

Results (typical):

• Best thickness: ~4.2mm
• Stress reduction: 15-20% vs. baseline
• Convergence: ~30 trials to plateau

Dashboard Usage

Start the dashboard:

python dashboard/start_dashboard.py

Features:

• Create studies with folder structure (sim/, results/, config.json)
• Drop .sim/.prt files into study folders
• Explore .sim files to extract expressions via NX
• Configure optimization with 5-step wizard:
  1. Simulation files
  2. Design variables
  3. Objectives
  4. Constraints
  5. Optimization settings
• Monitor progress with real-time charts
• View results with trial history and best parameters

Vision: LLM-Native Engineering Assistant

Atomizer is evolving into a comprehensive AI-powered engineering platform. See DEVELOPMENT_ROADMAP.md for details on:

• Phase 1-7 development plan with timelines and deliverables
• Example use cases demonstrating natural language workflows
• Architecture diagrams showing plugin system and LLM integration
• Success metrics for each phase

Future Capabilities

User: "Add RSS function combining stress and displacement"
→ LLM: Writes Python function, validates, registers as custom objective

User: "Use surrogate to predict these 10 parameter sets"
→ LLM: Checks surrogate R² > 0.9, runs predictions with confidence intervals

User: "Make an optimization report"
→ LLM: Generates HTML with plots, insights, recommendations (30 seconds)

User: "Why did trial #34 perform best?"
→ LLM: "Trial #34 had optimal stress distribution due to thickness 4.2mm
       creating uniform load paths. Fillet radius 3.1mm reduced stress
       concentration by 18%. This combination is Pareto-optimal."

Development Status

Completed Phases

• Phase 1: Core optimization engine & Plugin system ✅

  • NX journal integration
  • Web dashboard
  • Lifecycle hooks (pre-solve, post-solve, post-extraction)

• Phase 2.5: Intelligent Codebase-Aware Gap Detection ✅

  • Scans existing capabilities before requesting examples
  • Matches workflow steps to implemented features
  • 80-90% accuracy on complex optimization requests

• Phase 2.6: Intelligent Step Classification ✅

  • Distinguishes engineering features from inline calculations
  • Identifies post-processing hooks vs FEA operations
  • Foundation for smart code generation

• Phase 2.7: LLM-Powered Workflow Intelligence ✅

  • Replaces static regex with Claude AI analysis
  • Detects ALL intermediate calculation steps
  • Understands engineering context (PCOMP, CBAR, element forces, etc.)
  • 95%+ expected accuracy with full nuance detection

Next Priorities

• Phase 2.8: Inline Code Generation - Auto-generate simple math operations
• Phase 2.9: Post-Processing Hook Generation - Middleware script generation
• Phase 3: MCP Integration - Automated research from NX/pyNastran docs
• Phase 4: Code generation for complex FEA features
• Phase 5: Analysis & decision support
• Phase 6: Automated reporting

For Developers:

• DEVELOPMENT.md - Current status, todos, and active development
• DEVELOPMENT_ROADMAP.md - Strategic vision and long-term plan
• CHANGELOG.md - Version history and changes

License

Proprietary - Atomaste © 2025

Support

• Documentation: docs/
• Studies: studies/ - Optimization study templates and examples
• Development Roadmap: DEVELOPMENT_ROADMAP.md
• Email: antoine@atomaste.com

Resources

NXOpen References

• Official API Docs: Siemens NXOpen Documentation
• NXOpenTSE: The Scripting Engineer's Guide
• Our Guide: NXOpen Resources

Optimization

• Optuna Documentation: optuna.readthedocs.io
• pyNastran: github.com/SteveDoyle2/pyNastran

---

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