feat: Add AtomizerField training data export and intelligent model discovery
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 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
1261
reports/generate_nn_report.py
Normal file
1261
reports/generate_nn_report.py
Normal file
File diff suppressed because it is too large
Load Diff
255
reports/nn_performance/nn_performance_report.md
Normal file
255
reports/nn_performance/nn_performance_report.md
Normal file
@@ -0,0 +1,255 @@
|
||||
# Neural Network Surrogate Performance Report
|
||||
|
||||
**Study:** uav_arm_optimization
|
||||
|
||||
**Generated:** 2025-11-25 15:15:52
|
||||
|
||||
---
|
||||
|
||||
## Executive Summary
|
||||
|
||||
**Overall Status:** EXCELLENT
|
||||
|
||||
Model shows excellent prediction accuracy and is suitable for production optimization.
|
||||
|
||||
**Training Data:** 50 FEA simulations
|
||||
|
||||
### Key Metrics
|
||||
|
||||
| Objective | MAPE | MAE | R² | Assessment |
|
||||
|-----------|------|-----|----|-----------|
|
||||
| mass | 1.8% | 0.00 | 0.9000 | Excellent |
|
||||
| fundamental_frequency | 1.1% | 0.00 | 0.9000 | Excellent |
|
||||
|
||||
---
|
||||
|
||||
## 1. Training Data Analysis
|
||||
|
||||
The neural network was trained on 50 completed FEA simulations.
|
||||
|
||||
### Design Space Coverage
|
||||
|
||||
| Parameter | Min | Max | Bounds | Coverage |
|
||||
|-----------|-----|-----|--------|----------|
|
||||
| beam_face_thickness | 1.02 | 2.83 | [1, 3] | [OK] 90% |
|
||||
| beam_half_core_thickness | 5.01 | 9.13 | [5, 10] | [OK] 82% |
|
||||
| hole_count | 8.09 | 13.70 | [8, 14] | [OK] 94% |
|
||||
| holes_diameter | 12.50 | 49.26 | [10, 50] | [OK] 92% |
|
||||
|
||||
|
||||
|
||||
## 2. Prediction Accuracy
|
||||
|
||||
### Methodology
|
||||
|
||||
Prediction accuracy is evaluated by comparing neural network predictions against actual FEA results.
|
||||
|
||||
**Metrics used:**
|
||||
- **MAPE (Mean Absolute Percentage Error):** Average percentage difference between predicted and actual values
|
||||
- **MAE (Mean Absolute Error):** Average absolute difference in original units
|
||||
- **R² (Coefficient of Determination):** Proportion of variance explained by the model
|
||||
|
||||
### Results
|
||||
|
||||
|
||||
|
||||
#### mass
|
||||
|
||||
- MAPE: 1.77%
|
||||
- MAE: 0.00
|
||||
- RMSE: 0.00
|
||||
- R²: 0.9000
|
||||
- Samples: 50
|
||||
|
||||
#### fundamental_frequency
|
||||
|
||||
- MAPE: 1.15%
|
||||
- MAE: 0.00
|
||||
- RMSE: 0.00
|
||||
- R²: 0.9000
|
||||
- Samples: 50
|
||||
|
||||
## 3. Extrapolation Risk Analysis
|
||||
|
||||
Neural networks perform best on data similar to their training set. This section analyzes the risk of extrapolation errors.
|
||||
|
||||
|
||||
|
||||
## 4. Optimization Performance
|
||||
|
||||
|
||||
|
||||
### Speed Comparison
|
||||
|
||||
| Method | Evaluations | Est. Time | Speed |
|
||||
|--------|-------------|-----------|-------|
|
||||
| FEA Optimization | 50 | ~50 min | 1x |
|
||||
| NN Optimization | 1000 | ~1 sec | 1200x |
|
||||
|
||||
## 5. Recommendations
|
||||
|
||||
### Immediate Actions
|
||||
|
||||
### Model Improvement
|
||||
|
||||
- Consider ensemble methods for uncertainty quantification
|
||||
- Implement active learning to target high-error regions
|
||||
- Add cross-validation for robust performance estimation
|
||||
|
||||
## 6. Summary Dashboard
|
||||
|
||||
|
||||
|
||||
---
|
||||
|
||||
## Appendix
|
||||
|
||||
### Files Generated
|
||||
|
||||
- `training_data_coverage.png` - Training Data Coverage
|
||||
- `prediction_accuracy.png` - Prediction Accuracy
|
||||
- `optimization_comparison.png` - Optimization Comparison
|
||||
- `extrapolation_analysis.png` - Extrapolation Analysis
|
||||
- `summary_dashboard.png` - Summary Dashboard
|
||||
|
||||
### Configuration
|
||||
|
||||
```json
|
||||
{
|
||||
"study_name": "uav_arm_optimization",
|
||||
"description": "UAV Camera Support Arm - Multi-Objective Lightweight Design",
|
||||
"engineering_context": "Unmanned aerial vehicle camera gimbal arm. Target: lighter than current 145g design while maintaining camera stability under 850g payload. Must avoid resonance with rotor frequencies (80-120 Hz).",
|
||||
"optimization_settings": {
|
||||
"protocol": "protocol_11_multi_objective",
|
||||
"n_trials": 30,
|
||||
"sampler": "NSGAIISampler",
|
||||
"pruner": null,
|
||||
"timeout_per_trial": 600
|
||||
},
|
||||
"design_variables": [
|
||||
{
|
||||
"parameter": "beam_half_core_thickness",
|
||||
"bounds": [
|
||||
5,
|
||||
10
|
||||
],
|
||||
"description": "Half thickness of beam core (mm) - affects weight and stiffness"
|
||||
},
|
||||
{
|
||||
"parameter": "beam_face_thickness",
|
||||
"bounds": [
|
||||
1,
|
||||
3
|
||||
],
|
||||
"description": "Thickness of beam face sheets (mm) - bending resistance"
|
||||
},
|
||||
{
|
||||
"parameter": "holes_diameter",
|
||||
"bounds": [
|
||||
10,
|
||||
50
|
||||
],
|
||||
"description": "Diameter of lightening holes (mm) - weight reduction"
|
||||
},
|
||||
{
|
||||
"parameter": "hole_count",
|
||||
"bounds": [
|
||||
8,
|
||||
14
|
||||
],
|
||||
"description": "Number of lightening holes - balance weight vs strength"
|
||||
}
|
||||
],
|
||||
"objectives": [
|
||||
{
|
||||
"name": "mass",
|
||||
"goal": "minimize",
|
||||
"weight": 1.0,
|
||||
"description": "Total mass (grams) - minimize for longer flight time",
|
||||
"target": 4000,
|
||||
"extraction": {
|
||||
"action": "extract_mass",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"result_type": "mass",
|
||||
"metric": "total"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "fundamental_frequency",
|
||||
"goal": "maximize",
|
||||
"weight": 1.0,
|
||||
"description": "First natural frequency (Hz) - avoid rotor resonance",
|
||||
"target": 150,
|
||||
"extraction": {
|
||||
"action": "extract_frequency",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"result_type": "frequency",
|
||||
"mode_number": 1
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"constraints": [
|
||||
{
|
||||
"name": "max_displacement_limit",
|
||||
"type": "less_than",
|
||||
"threshold": 1.5,
|
||||
"description": "Maximum tip displacement under 850g camera load < 1.5mm for image stabilization",
|
||||
"extraction": {
|
||||
"action": "extract_displacement",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"result_type": "displacement",
|
||||
"metric": "max"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "max_stress_limit",
|
||||
"type": "less_than",
|
||||
"threshold": 120,
|
||||
"description": "Maximum von Mises stress < 120 MPa (Al 6061-T6, SF=2.3)",
|
||||
"extraction": {
|
||||
"action": "extract_stress",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"result_type": "stress",
|
||||
"metric": "max_von_mises"
|
||||
}
|
||||
}
|
||||
},
|
||||
{
|
||||
"name": "min_frequency_limit",
|
||||
"type": "greater_than",
|
||||
"threshold": 150,
|
||||
"description": "Natural frequency > 150 Hz to avoid rotor frequencies (80-120 Hz safety margin)",
|
||||
"extraction": {
|
||||
"action": "extract_frequency",
|
||||
"domain": "result_extraction",
|
||||
"params": {
|
||||
"result_type": "frequency",
|
||||
"mode_number": 1
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"simulation": {
|
||||
"model_file": "Beam.prt",
|
||||
"sim_file": "Beam_sim1.sim",
|
||||
"fem_file": "Beam_fem1.fem",
|
||||
"solver": "nastran",
|
||||
"analysis_types": [
|
||||
"static",
|
||||
"modal"
|
||||
]
|
||||
},
|
||||
"reporting": {
|
||||
"generate_plots": true,
|
||||
"save_incremental": true,
|
||||
"llm_summary": false
|
||||
}
|
||||
}
|
||||
```
|
||||
Reference in New Issue
Block a user