femtosecond-laser-hydrogel-etching-model

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)

RL Optimizer (SAC v2 - Depth + Width Focus)

Design Principles (from literature)

Based on published RL-for-laser research:

• SAC dominates PPO/TD3 for continuous laser control (TempoRL, arxiv:2304.12187)
• Delta actions (±15% of param range/step) for machine safety (arxiv: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)

HAMA (25% of data)

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:

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:

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

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
3. Capuano et al., "Shaping Laser Pulses with RL", arxiv:2503.00499
4. "Optimizing Operation Recipes with RL", arxiv: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

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