Cutting operations are important and commonly used operations in the field of manufacturing. Automated machining is today commonly used in CNC-machines. One common drawback with automated machining is that the tool condition is challenging to predict which leads to a conservative tool replacement times. This leads to a low utilisation of the tool economical lifetime and an unnecessary high number of tool replacements. Methods for indirect continuous monitoring of the tool wear exist but usually require retrofitting of external sensors that can be both costly and also interrupt the machine operation due to the additional wiring. It is therefore of interest to investigate the possibility to use the, often high resolution, sensors already fitted in a CNC-machine to extract valuable data that can indirectly give an estimation of the tool condition.

This thesis work has, with attention to the X-, Y- and Z-position sensors, resulted in development of algorithms that show relations between tool wear and data acquired from these sensors. The algorithms operate in the frequency domain to determine changes in the dynamic response over the time of tool degradation.