Sensor-equipped cutting tools enhance metal machining by allowing real-time monitoring of cutting forces, tool deflection, vibrations, and tool condition, improving process control and tool life. However, challenges such as noise, transfer path distortion, and inaccurate force estimation due to tool wear limit current solutions. This research integrates cutting force models, signal processing, and system identification to enhance measurement accuracy, prediction capabilities, and real-time monitoring for machining optimization.

This thesis establishes a framework to enhance the performance and reliability of sensor-equipped cutting tools by addressing how tool dynamics affect sensor data. Improving measurement quality improves the predictive capabilities of these tools, making them adaptable to various cutting tool configurations and applications.

A key contribution is an extended Kienzle-Sağlam force model that incorporates tool wear effects, enabling precise cutting force predictions and real-time tool wear monitoring. Additionally, an analytical approach for modeling strain-force transfer functions in metal cutting tools, combined with inverse filtering, corrects signal distortions in dynamic load estimations caused by tool dynamics. The developed methods can be used to improve the accuracy when estimating dynamic loads and tool-tip deflection, addressing limitations of statically calibrated systems.

This thesis presents a model-based method that accurately estimates dynamic loads and displacements in sensor-equipped cutting tools using strain response data. Validated through simulations and experiments, this method provides a foundation for continuing research aimed at adapting it for real-world applications, supporting the in-process monitoring of tool condition, machining stability, and surface quality.