Transforming smart factories: real-time optimisation of cyber-physical systems with digital twins

Smart factories represent the forefront of modern manufacturing, driven by advancements in technology and the integration of digital tools with physical systems using Industrial Internet of Things (IIoT) solutions. This work presents the integration of Cyber-Physical Systems (CPS) with Digital Twin (DT) technology to enhance the efficiency and real-time optimisation within a smart factory assembly line environment. Central to the study is the automation and integration of a Siemens-Festo Assembly (SFA) within a smart manufacturing lab, where the primary goal is to establish a robust system for seamless data retrieval and communication between the CPS and its DT. The SFA is equipped with multiple sensors that monitor and control various stages of production assembly and is controlled by stage-by-stage programmable logic controller (PLC)’s. These sensors mimic the IIoT setting and produce vital data aiding the monitoring of processes in real time. Programming environments, such as TIA-Portal and Codesys facilitates the extraction of sensor data i.e., the PLC Tags, that are exported by OPC-UA protocols via Node-RED, to entities outside the closed system. The DT, a simulation model created with Unity, receives this data via a TCP connection, guaranteeing that the physical functions of the Siemens-Festo machine are accurately reflected. An environment for visualizing, tracking, and real-time process optimization in manufacturing is provided by the DT model. It is designed in Blender and incorporated into Unity to produce a detailed model of the SFA. The project shows how these technologies can be successfully integrated, allowing real-time sensor data from the physical machine to be accurately mirrored to the DT. To support the safe and effective operation of a smart factory, this data is further examined and stored.

This version of the paper has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-science/policies/accepted-man...), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-76452-3_22