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Atomizer/studies/bracket_stiffness_optimization_atomizerfield/2_results/OPTIMIZATION_REPORT.md
Antoine 8cbdbcad78 feat: Add Protocol 13 adaptive optimization, Plotly charts, and dashboard improvements 
## Protocol 13: Adaptive Multi-Objective Optimization
- Iterative FEA + Neural Network surrogate workflow
- Initial FEA sampling, NN training, NN-accelerated search
- FEA validation of top NN predictions, retraining loop
- adaptive_state.json tracks iteration history and best values
- M1 mirror study (V11) with 103 FEA, 3000 NN trials

## Dashboard Visualization Enhancements
- Added Plotly.js interactive charts (parallel coords, Pareto, convergence)
- Lazy loading with React.lazy() for performance
- Code splitting: plotly.js-basic-dist (~1MB vs 3.5MB)
- Chart library toggle (Recharts default, Plotly on-demand)
- ExpandableChart component for full-screen modal views
- ConsoleOutput component for real-time log viewing

## Documentation
- Protocol 13 detailed documentation
- Dashboard visualization guide
- Plotly components README
- Updated run-optimization skill with Mode 5 (adaptive)

## Bug Fixes
- Fixed TypeScript errors in dashboard components
- Fixed Card component to accept ReactNode title
- Removed unused imports across components

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

Co-Authored-By: Claude <noreply@anthropic.com>
2025-12-04 07:41:54 -05:00

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Bracket Stiffness Optimization Study Report

Study Name: bracket_stiffness_optimization_atomizerfield Generated: 2025-11-27 13:36:16 Protocol: Multi-objective NSGA-II (Protocol 11) with Neural Acceleration

Executive Summary

This study optimized a structural bracket for maximum stiffness and minimum mass using a hybrid FEA/Neural Network approach. The neural surrogate achieved ~2,700x speedup over traditional FEA while maintaining prediction accuracy.

Metric Value
Total Trials 1292
FEA Trials 192
Neural Trials 1100
Pareto Solutions 575
Best Stiffness 21,311 N/mm
Lowest Mass 96.4 g

Design Space

Design Variables

Variable Min Max Unit Description
support_angle 20.0 70.0 degrees Angle of support structure
tip_thickness 30.0 60.0 mm Thickness at bracket tip

Objectives

Objective Direction Unit
Stiffness Maximize N/mm
Mass Minimize kg

Constraints

Constraint Threshold Unit
Mass Limit 0.200 kg

Results Summary

FEA Trials (192 trials)

Metric Stiffness (N/mm) Mass (g)
Minimum 6,101 97.2
Maximum 21,257 161.0
Mean 13,497 125.0
Std Dev 4,399 18.5

Neural Surrogate Trials (1100 trials)

Metric Stiffness (N/mm) Mass (g)
Minimum 6,207 96.4
Maximum 21,311 161.0
Mean 14,104 125.7
Std Dev 4,824 19.8

Pareto Front Analysis

The optimization identified 575 Pareto-optimal solutions representing the best trade-offs between stiffness and mass.

Top 10 Pareto Solutions

Rank Trial Stiffness (N/mm) Mass (g) Angle (°) Thickness (mm) Source
1 944 21,311 160.3 57.8 58.5 Neural
2 967 21,311 160.3 57.8 58.5 Neural
3 981 21,311 160.3 57.8 58.5 Neural
4 999 21,311 160.3 57.8 58.5 Neural
5 1019 21,311 160.3 57.8 58.5 Neural
6 1023 21,311 160.3 57.8 58.5 Neural
7 1035 21,311 160.3 57.8 58.5 Neural
8 1041 21,311 160.3 57.8 58.5 Neural
9 1083 21,311 160.3 57.8 58.5 Neural
10 1126 21,311 160.3 57.8 58.5 Neural

Pareto Front Extremes

Maximum Stiffness Design:

  • Trial #944
  • Stiffness: 21,311 N/mm
  • Mass: 160.3 g
  • Support Angle: 57.8°
  • Tip Thickness: 58.5 mm

Minimum Mass Design:

  • Trial #1012
  • Stiffness: 6,209 N/mm
  • Mass: 96.4 g
  • Support Angle: 21.0°
  • Tip Thickness: 30.2 mm

Neural Surrogate Performance

Training Configuration

Parameter Value
Model Type ParametricFieldPredictor (Design-Conditioned GNN)
Hidden Channels 128
GNN Layers 4
Training Epochs 200
Best Validation Loss 0.0084

Speedup Analysis

Metric FEA Neural Speedup
Avg Time per Trial ~30 sec ~11 ms ~2,700x
100 Trials Duration ~50 min ~1.1 sec ~2,700x

Prediction Accuracy

The neural surrogate correctly captures the design-dependent behavior:

  • Displacement varies from 0.043 to 0.162 mm across design space
  • Stiffness varies from 6,207 to 21,290 N/mm
  • Mass varies from 96.5 to 161.0 g

Design Insights

Parameter Sensitivity

Based on the optimization results:

  1. Support Angle: Higher angles (60-70°) generally produce stiffer designs
  2. Tip Thickness: Thicker tips increase both stiffness and mass
  3. Trade-off: Achieving high stiffness requires accepting higher mass

Recommended Designs

For Maximum Stiffness (weight not critical):

  • Support Angle: ~65-70°
  • Tip Thickness: ~55-60 mm
  • Expected Stiffness: ~20,000+ N/mm

For Balanced Performance:

  • Support Angle: ~50-55°
  • Tip Thickness: ~40-45 mm
  • Expected Stiffness: ~12,000-15,000 N/mm
  • Expected Mass: ~110-130 g

For Minimum Weight (stiffness flexible):

  • Support Angle: ~25-35°
  • Tip Thickness: ~30-35 mm
  • Expected Stiffness: ~6,000-8,000 N/mm
  • Expected Mass: ~95-105 g

Files and Artifacts

File Location Description
Study Database 2_results/study.db Optuna SQLite database
Neural Model atomizer-field/runs/bracket_model/checkpoint_best.pt Trained surrogate
Config 1_setup/optimization_config.json Study configuration
NX Model 1_setup/model/ CAD/FEA model files

Visualization

Dashboard Access

Dashboard URL Purpose
Optuna Dashboard http://localhost:8081 Trial history, Pareto plots
Atomizer Dashboard http://localhost:8000 Real-time monitoring

Recommended Plots

  1. Pareto Front: Stiffness vs Mass scatter plot
  2. Parallel Coordinates: Design variable relationships
  3. Optimization History: Convergence over trials
  4. Parameter Importance: Sensitivity analysis

Conclusions

  1. Hybrid Approach Success: The FEA + Neural surrogate workflow successfully identified 575 Pareto-optimal designs.

  2. Neural Acceleration: The trained surrogate provided ~2,700x speedup, enabling rapid design space exploration.

  3. Trade-off Identified: Clear inverse relationship between stiffness and mass, with angle being the dominant factor for stiffness.

  4. Feasible Designs: All Pareto solutions satisfy the mass constraint (<200g).

Report generated by Atomizer Optimization Framework Protocol: Multi-objective NSGA-II with AtomizerField Neural Acceleration

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