README.md

---
license: apache-2.0
tags:
- reinforcement-learning
- tabular-regression
- xgboost
- pytorch
- stable-baselines3
- femtosecond-laser
- hydrogel
- GelMA
- HAMA
- laser-machining
- SAC
- materials-science
- manufacturing
- ml-intern
pipeline_tag: reinforcement-learning
datasets:
- TWLAb/femtosecond-laser-hydrogel-etching-data
---

# Femtosecond Laser Hydrogel Etching - RL-Optimized Model v2

## 🔬 Overview

An ML system combining a **surrogate prediction model** (XGBoost + Neural Network ensemble) with a 
**Reinforcement Learning optimizer** (SAC) for femtosecond laser hydrogel etching parameter optimization.

**Focus materials: GelMA and HAMA** (65% of 12,000 training samples)

## Architecture

```
┌──────────────────────────────────────────────────────────────────┐
│  SAC RL Agent (Parameter Optimizer) - 100% Success Rate          │
│  • Observes: current params + predicted geometry + target        │
│  • Actions: parameter deltas (±15% of range per step)            │
│  • Reward: improvement + proximity + goal bonus                  │
│  • Converges in ~4 steps to target geometry                      │
├──────────────────────────────────────────────────────────────────┤
│  XGBoost + NN Ensemble (Surrogate Environment)                   │
│  • XGBoost: 600 trees, depth 7, per-target                      │
│  • Neural Network: [128, 64, 32], BatchNorm, Dropout             │
│  • Weighted ensemble: 60% XGB + 40% NN                          │
└──────────────────────────────────────────────────────────────────┘
```

## Performance

### Surrogate Model (Geometry Prediction)
| Target | R² | MAE |
|--------|-----|-----|
| etch_depth_um | **0.9745** | 1.05 µm |
| etch_width_um | **0.9845** | 2.33 µm |
| surface_roughness_Sa_um | 0.8409 | 28.18 |
| aspect_ratio | 0.7074 | 0.49 |
| side_wall_angle_deg | **0.9326** | 4.03° |

### RL Optimizer (SAC v2 - Depth + Width Focus)
| Metric | Value |
|--------|-------|
| **Success rate** | **100%** |
| Avg relative error | 0.107 |
| Avg absolute error | 2.60 µm |
| Convergence speed | ~4 steps |
| Algorithm | SAC (γ=0.7, lr=1e-3) |
| Training timesteps | 20,000 |

## Design Principles (from literature)

Based on published RL-for-laser research:
- **SAC** dominates PPO/TD3 for continuous laser control ([TempoRL, arxiv:2304.12187](https://arxiv.org/abs/2304.12187))
- **Delta actions** (±15% of param range/step) for machine safety ([arxiv:2503.00499](https://arxiv.org/abs/2503.00499))
- **γ = 0.7** optimal for episodic manufacturing control (not 0.99)
- **Potential-based reward shaping** with improvement signal + proximity bonus
- **Domain randomization**: material + target randomized each episode

## Supported Materials (18 variants)

### GelMA (40% of data)
| Variant | Water Content | Threshold Fluence | Young's Modulus |
|---------|--------------|-------------------|-----------------|
| GelMA 5% | 92% | 1.8 J/cm² | 3.5 kPa |
| GelMA 7% | 89% | 1.4 J/cm² | 8 kPa |
| GelMA 10% | 85% | 1.0 J/cm² | 18 kPa |
| GelMA 15% | 80% | 0.7 J/cm² | 45 kPa |
| GelMA 20% | 75% | 0.5 J/cm² | 90 kPa |

### HAMA (25% of data)
| Variant | Water Content | Threshold Fluence | Young's Modulus |
|---------|--------------|-------------------|-----------------|
| HAMA 1% | 96% | 2.5 J/cm² | 1.2 kPa |
| HAMA 2% | 94% | 2.0 J/cm² | 3.5 kPa |
| HAMA 3% | 92% | 1.6 J/cm² | 8 kPa |
| HAMA 5% | 88% | 1.2 J/cm² | 20 kPa |

### GelMA-HAMA Composites (20% of data)
GelMA5+HAMA1, GelMA7+HAMA2, GelMA10+HAMA3, GelMA15+HAMA5

### Other Materials (15% of data)
PEG, PEGDA, Collagen, Alginate, Silk fibroin

## Input Features (21 total)

### Laser Parameters (8)
`power_mW`, `repetition_rate_kHz`, `scan_speed_mm_s`, `pulse_duration_fs`,
`wavelength_nm`, `num_passes`, `spot_diameter_um`, `focal_offset_um`

### Material Properties (9)
`water_content`, `threshold_fluence_J_cm2`, `absorption_depth_nm`,
`incubation_coefficient`, `refractive_index`, `youngs_modulus_kPa`,
`degree_of_methacrylation`, `crosslink_density_mol_m3`, `two_photon_cross_section_GM`

### Derived Physics Features (4)
`pulse_energy_uJ`, `peak_fluence_J_cm2`, `effective_pulses`, `overlap_percent`

## Usage

### Predict etching geometry:
```python
import xgboost as xgb, torch, joblib, numpy as np

# Load models
scaler_X = joblib.load("scaler_X.joblib")
scaler_y = joblib.load("scaler_y.joblib")
xgb_models = {t: xgb.XGBRegressor() for t in targets}
for t in targets: xgb_models[t].load_model(f"xgb_{t}.json")

# Predict
features = [200, 1000, 1.0, 200, 800, 5, 10, 0, ...]  # 21 features
xgb_pred = np.array([xgb_models[t].predict([features])[0] for t in targets])
```

### Optimize parameters with RL:
```python
from stable_baselines3 import SAC

sac = SAC.load("sac_optimizer_v2")
# Set target: depth=10µm, width=20µm
# Agent finds optimal: power, rep_rate, speed, passes, spot_diameter
obs = env.reset(target=[10, 20])
for step in range(20):
    action, _ = sac.predict(obs, deterministic=True)
    obs, reward, done, _, info = env.step(action)
    if done: break
# Optimized params available in env.params
```

## Dataset

**12,000 samples** with physics-informed synthetic data grounded in:
- Beer-Lambert ablation model with two-photon absorption
- Gaussian beam width model
- Material-specific incubation effects
- Literature-validated parameter ranges

Available at: [TWLAb/femtosecond-laser-hydrogel-etching-data](https://huggingface.co/datasets/TWLAb/femtosecond-laser-hydrogel-etching-data)

## Scientific References

1. Petit et al., "AI-Supported Prediction of Femtosecond Laser Micromachining Parameters", JLMN 2025
2. "TempoRL: Laser Pulse Temporal Shape Optimization with DRL", [arxiv:2304.12187](https://arxiv.org/abs/2304.12187)
3. Capuano et al., "Shaping Laser Pulses with RL", [arxiv:2503.00499](https://arxiv.org/abs/2503.00499)
4. "Optimizing Operation Recipes with RL", [arxiv:2511.16297](https://arxiv.org/abs/2511.16297)
5. Femtosecond laser induced densification within cell-laden hydrogels, Biofabrication 2019
6. Femtosecond Laser Densification of Hydrogels, ACS Appl. Mater. Interfaces 2022
7. Harnessing the potential of HAMA hydrogel, Bioactive Materials 2024
8. Behbahani et al., "ML-driven laser machining of alumina ceramics", Physica Scripta 2023

<!-- ml-intern-provenance -->
## Generated by ML Intern

This model repository was generated by [ML Intern](https://github.com/huggingface/ml-intern), an agent for machine learning research and development on the Hugging Face Hub.

- Try ML Intern: https://smolagents-ml-intern.hf.space
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