Implementation of an Intelligent System Architecture for Process Monitoring of Machine Tools

State of the art machine tool controllers offer several Internet-of-Things (IoT) interfaces for machine data acquisition using industrial or edge computers. However, the available data exchange rates for these communication platforms are limited to a few hundred Hertz. As the data is not available in high frequency resolution, such a network communication is not suitable for monitoring and optimizing highly dynamic machining processes. This paper describes an efficient system architecture, which enables the acquisition of internal machine data as well as the high frequency sampling of external sensors. Based on this data, an Operational Modal Analysis (OMA) approach can be used to determine the tool tip dynamics during the machining process. Identification of tool tip frequency response requires the reconstruction of the excitation of the machine tool structure, i.e. the occurring machining forces. For this purpose, an approach relying on monitoring the commanded motor currents is applied.