- Simulates spindle performance across scenarios (High-Speed, High-Torque, Balanced) and time-based simulations.
- Generates dynamic load profiles with sinusoidal variation and random spikes to simulate real-world conditions.
- Uses a k-Nearest Neighbors (k-NN) algorithm with historical data to predict maintenance needs.
- Provides comprehensive reports with scenario summaries and recommendations (e.g., switch to liquid cooling if temperature is high).
- Generates a maintenance schedule based on bearing type, lubrication type, and operating hours.
- Includes tasks like bearing inspection, lubrication maintenance, vibration monitoring, alignment checks, and general maintenance.
- Shows performance metrics (vibration, temperature, power, load, bearing life, spindle life, wheel wear) as color-coded bars (green for acceptable, red for exceeding thresholds).
- Covers a wide range of spindle performance aspects (power, thermal, vibration, fatigue, wear) with realistic calculations.
- The k-NN-based maintenance prediction adds predictive maintenance capabilities, enhancing practical utility.
