Industrial machine process data (sensory)

To ensure high surface quality and low cost during production milling process, the tool ware has to be monitored constantly. However, manual inspection can be highly time consuming and erroneous. Simple automatic solutions are often not sufficient, since the tool ware behaves usually non-linear and depends on many factors.
The data can be employed in machine learning methods for indirect determination of tool ware / anomalies. The data is generated on a CNC machine in a multi interpolation manufacturing process and 3 tool ware classes were identified and labeled by experts.