Conclution

Conclusion

This study presents a comprehensive simulation-based approach to predictive tool wear monitoring by integrating vibration analysis with FEA-driven stress and modal evaluation. The results demonstrate that cutting speed, tool tip radius, and depth of cut significantly influence tool vibration frequency, equivalent stress, and deformation. Notably, higher speeds and deeper cuts tend to elevate stress and vibrational response, which are precursors to increased tool wear and potential failure. The findings validate that vibration frequency and deformation patterns, particularly in higher mode shapes, can serve as effective indicators for predicting tool wear progression. By linking simulation insights with real-time vibration data, this method can support the development of intelligent, non-intrusive tool condition monitoring systems. Future work may extend this framework by incorporating machine learning algorithms to correlate simulation outputs with sensor-acquired data for automated wear diagnostics.