Results

The system successfully simulated tool wear scenarios and generated realistic vibration and force signals based on machining parameters.

The vibration sensor module accurately reflected tool condition through changes in amplitude and frequency of the signal, showing higher vibration levels as tool wear increased.

The FEA + vibration analysis engine detected significant variations in stress and dynamic response, such as reduced stiffness and mode shape shifts, indicating progressive wear.

The predictive model reliably categorized the tool condition into healthy, moderately worn, and critical, based on signal features and simulation outputs.

The GUI interface displayed real-time tool status with alerts and logged data for future analysis, demonstrating the system’s readiness for integration into a smart manufacturing setup.