New E11 Part Mass Extractor:
- Add nx_journals/extract_part_mass_material.py - NX journal using
NXOpen.MeasureManager.NewMassProperties() for accurate geometry-based mass
- Add optimization_engine/extractors/extract_part_mass_material.py - Python
wrapper that reads JSON output from journal
- Add E11 entry to extractors/catalog.json
Documentation Updates:
- SYS_12_EXTRACTOR_LIBRARY.md: Add mass accuracy warning noting pyNastran
get_mass_breakdown() under-reports ~7% on hex-dominant meshes with
tet/pyramid fill elements. E11 (geometry .prt) should be preferred over
E4 (BDF) unless material is overridden at FEM level.
- 01_CHEATSHEET.md: Add mass extraction tip
V14 Config:
- Expand design variable bounds (blank_backface_angle max 4.5°,
whiffle_triangle_closeness max 80mm, whiffle_min max 60mm)
Testing showed:
- E11 from .prt: 97.66 kg (accurate - matches NX GUI)
- E4 pyNastran get_mass_breakdown(): 90.73 kg (~7% under-reported)
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Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
Add persistent knowledge system that enables Atomizer to learn from every
session and improve over time.
## New Files
- knowledge_base/lac.py: LAC class with optimization memory, session insights,
and skill evolution tracking
- knowledge_base/__init__.py: Package initialization
- .claude/skills/modules/learning-atomizer-core.md: Full LAC skill documentation
- docs/07_DEVELOPMENT/ATOMIZER_CLAUDE_CODE_INSTRUCTIONS.md: Master instructions
## Updated Files
- CLAUDE.md: Added LAC section, communication style, AVERVS execution framework,
error classification, and "Atomizer Claude" identity
- 00_BOOTSTRAP.md: Added session startup/closing checklists with LAC integration
- 01_CHEATSHEET.md: Added LAC CLI and Python API quick reference
- 02_CONTEXT_LOADER.md: Added LAC query section and anti-pattern
## LAC Features
- Query similar past optimizations before starting new ones
- Record insights (failures, success patterns, workarounds)
- Record optimization outcomes for future reference
- Suggest protocol improvements based on discoveries
- Simple JSONL storage (no database required)
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
- Reorganize dashboard: control panel on top, charts stacked vertically
- Add Set Context button to Claude terminal for study awareness
- Add conda environment instructions to CLAUDE.md
- Fix STUDY_REPORT.md location in generate-report.md skill
- Claude terminal now sends study context with skills reminder
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add trial limiting (300 max) and reduce polling to 15s for large studies
- Make dashboard layout wider with col-span adjustments
- Claude terminal now runs from Atomizer root for CLAUDE.md/skills access
- Add study context display in terminal on connect
- Add KaTeX math rendering styles for study reports
- Add surrogate tuner module for hyperparameter optimization
- Fix backend proxy to port 8001
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Co-Authored-By: Claude <noreply@anthropic.com>
- Add ConvergencePlot component with running best, statistics, gradient fill
- Add ParameterImportanceChart with Pearson correlation analysis
- Add StudyReportViewer with KaTeX math rendering and full markdown support
- Update pruning endpoint to query Optuna database directly
- Add /report endpoint for STUDY_REPORT.md files
- Fix chart data transformation for single/multi-objective studies
- Update Protocol 13 documentation with new components
- Update generate-report skill with dashboard integration
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Co-Authored-By: Claude <noreply@anthropic.com>
Major additions:
- Training data export system for AtomizerField neural network training
- Bracket stiffness optimization study with 50+ training samples
- Intelligent NX model discovery (auto-detect solutions, expressions, mesh)
- Result extractors module for displacement, stress, frequency, mass
- User-generated NX journals for advanced workflows
- Archive structure for legacy scripts and test outputs
- Protocol documentation and dashboard launcher
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Co-Authored-By: Claude <noreply@anthropic.com>
Phase 1.3.1 Complete - Logging Integration:
1. Updated .claude/skills/create-study.md:
- Added IMPORTANT section on structured logging from Phase 1.3
- Documents logger import and initialization
- Lists all structured logging methods (trial_start, trial_complete, etc.)
- References drone_gimbal_arm as template
2. Created studies/uav_arm_optimization/:
- Multi-objective NSGA-II study (50 trials)
- Same type as drone_gimbal_arm but renamed for UAV context
- Full integration with Phase 1.3 logging system
- Configuration: minimize mass + maximize frequency
- Running to validate complete logging system
Benefits:
- All future studies created via skill will have consistent logging
- Production-ready error handling and file logging from day 1
- Color-coded console output for better monitoring
- Automatic log rotation (50MB, 3 backups)
Related: Phase 1.2 (Configuration), Phase 1.3 (Logger), Phase 1.3.1 (Integration)
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Co-Authored-By: Claude <noreply@anthropic.com>
- New create-study.md skill for complete study scaffolding
- Interactive discovery process for problem understanding
- Automated generation of all study infrastructure:
- optimization_config.json with protocol selection
- workflow_config.json for future intelligent workflows
- run_optimization.py with proper multi-objective/multi-solution support
- reset_study.py for database management
- README.md with comprehensive documentation
- NX_FILE_MODIFICATIONS_REQUIRED.md when needed
- Protocol selection guidance (Protocol 10 vs 11)
- Extractor mapping to centralized library
- Multi-solution workflow detection
- Dashboard integration instructions
- User interaction best practices with confirmation steps
- Common patterns and critical reminders
- Reference to existing studies as templates
Enables users to create complete, working optimization studies
from natural language descriptions with proper Claude-guided workflow.
🤖 Generated with [Claude Code](https://claude.com/claude-code)
Co-Authored-By: Claude <noreply@anthropic.com>
Critical bug fix for LLM mode optimization:
**Problem**:
- NXParameterUpdater.update_expressions() uses NX journal to import expressions (default use_nx_import=True)
- The NX journal directly updates the PRT file on disk and saves it
- But then run_optimization.py was calling updater.save() afterwards
- save() writes self.content (loaded at initialization) back to file
- This overwrote the NX journal changes with stale binary content!
**Result**: All optimization trials produced identical FEM results because the model was never actually updated.
**Fixes**:
1. Removed updater.save() call from model_updater closure in run_optimization.py
2. Added theSession.Parts.CloseAll() in import_expressions.py to ensure changes are flushed and file is released
3. Fixed test_phase_3_2_e2e.py variable name (best_trial_file → results_file)
**Testing**: Verified expressions persist to disk correctly with standalone test.
Next step: Address remaining issue where FEM results are still identical (likely solve journal not reloading updated PRT).
- Add DEVELOPMENT_ROADMAP.md with 7-phase plan for LLM-driven optimization
- Phase 1: Plugin system with lifecycle hooks
- Phase 2: Natural language configuration interface
- Phase 3: Dynamic code generation for custom objectives
- Phase 4: Intelligent analysis and decision support
- Phase 5: Automated HTML/PDF reporting
- Phase 6: NX MCP server integration
- Phase 7: Self-improving feature registry
- Update README.md to reflect LLM-native philosophy
- Emphasize natural language workflows
- Link to development roadmap
- Update architecture diagrams
- Add future capability examples
- Reorganize documentation structure
- Move old dev docs to docs/archive/
- Clean up root directory
- Preserve all working optimization engine code
This sets the foundation for transforming Atomizer into an AI-powered
engineering assistant that can autonomously configure optimizations,
generate custom analysis code, and provide intelligent recommendations.
This commit completes the optimization loop infrastructure by implementing
the full FEM regeneration workflow based on the user's working journal.
## Changes
### FEM Regeneration Workflow (solve_simulation.py)
- Added STEP 1: Switch to Bracket.prt and update geometry
- Uses SetActiveDisplay() to make Bracket.prt active
- Calls UpdateManager.DoUpdate() to rebuild CAD geometry with new expressions
- Added STEP 2: Switch to Bracket_fem1 and update FE model
- Uses SetActiveDisplay() to make FEM active
- Calls fEModel1.UpdateFemodel() to regenerate FEM with updated geometry
- Added STEP 3: Switch back to sim part before solving
- Close and reopen .sim file to force reload from disk
### Enhanced Journal Output (nx_solver.py)
- Display journal stdout output for debugging
- Shows all journal steps: geometry update, FEM regeneration, solve, save
- Helps verify workflow execution
### Verification Tools
- Added verify_parametric_link.py journal to check expression dependencies
- Added FEM_REGENERATION_STATUS.md documenting the complete status
## Status
### ✅ Fully Functional Components
1. Parameter updates - nx_updater.py modifies .prt expressions
2. NX solver - ~4s per solve via journal
3. Result extraction - pyNastran reads .op2 files
4. History tracking - saves to JSON/CSV
5. Optimization loop - Optuna explores parameter space
6. **FEM regeneration workflow** - Journal executes all steps successfully
### ❌ Remaining Issue: Expressions Not Linked to Geometry
The optimization returns identical stress values (197.89 MPa) for all trials
because the Bracket.prt expressions are not referenced by any geometry features.
Evidence:
- Journal verification shows FEM update steps execute successfully
- Feature dependency check shows no features reference the expressions
- All optimization infrastructure is working correctly
The code is ready - waiting for Bracket.prt to have its expressions properly
linked to the geometry features in NX.
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Co-Authored-By: Claude <noreply@anthropic.com>
