Implementation and Evaluation of 5G-enabled sensors for Machine Tools

Abstract

Cyber-physical production systems (CPPS) are highly complex and difficult to manage systems. To address these issues, sensors have to be integrated to collect data and monitor processes, and thus provide in-depth intelligence for decision-making processes. The integration of sensors is a major challenge due to the infrastructure required for connectivity and flexibility. However, in existing manufacturing systems, the infrastructure for comprehensive data acquisition does not exist. Expanding the cable infrastructure to a sufficient level leads to high individual deployment and maintenance efforts. Approaches to implement different wireless infrastructure for different use cases lead to a high heterogeneity of communication technologies. 5G meets the networking requirements for many industrial use cases. This enables the flexible, wireless realization of process-integrated sensors. However, currently no implemented 5G-based data acquisition and utilization for a machine tool exists. In this paper, a framework for 5G-enabled sensors is developed and implemented for a machine tool. An existing digital twin for a milling machine is adapted to implement data-driven analysis. An accelerometer is 5G-enabled to transfer data in real-time for evaluation and visualization. Moreover, a smartphone-based sensor with 5G capability collects data of the machining process. This paper outlines the components needed for 5G-enabled data acquisition and analysis and aims to elaborate the benefits, potentials, and transferability of 5G sensors for further use cases.