Adaptive Joint Stress Predictor v1
Overview
Model ini dirancang untuk memprediksi tingkat stres pada sendi humanoid berdasarkan parameter gerakan, beban, dan kondisi lingkungan.
Tujuan utama:
- Mencegah overloading sendi
- Mengurangi risiko kerusakan mekanis
- Mendukung predictive maintenance system
Problem Type
Regression (Single Output)
Input Features
- joint_angle_deg
- angular_velocity_deg_per_sec
- payload_weight_kg
- movement_acceleration_mps2
- terrain_incline_deg
- ambient_temperature_celsius
- repetition_cycle_count
Output
- predicted_joint_stress_level (0-100 scale)
Model Architecture
- Input Normalization Layer
- Dense(256) + ReLU
- BatchNormalization
- Dense(128) + ReLU
- Dropout(0.3)
- Dense(64) + ReLU
- Dense(1) + Linear
Loss Function
Mean Squared Error (MSE)
Optimizer
Adam (learning_rate=0.0009)
Metrics
- MAE
- RMSE
- R² Score
Training Strategy
- 85/15 Train-Validation Split
- Early Stopping (patience=10)
- Learning Rate Reduce on Plateau
Expected Performance (Simulated)
- R²: ~0.93
- Low MAE on stress scale
- Stable convergence under 70 epochs
Deployment Scenario
- Real-time joint monitoring system
- Mechanical fatigue prevention module
- Edge AI embedded robotic controller
Version
v1 – Production-ready baseline