Extending the Digital Twin Ecosystem: A real-time Digital Twin of a LinuxCNC-controlled subtractive manufacturing machine

Abstract

This research paper describes the design and implementation of a real-time data-driven Digital Twin Ecosystem (DTE) for a LinuxCNC-controlled subtractive manufacturing computer numerical control (CNC) machine. The DTE architecture integrates real-time data acquisition, processing, and synchronization with the physical CNC machine. Our architecture adopts and extends the methodology presented in previous work and utilizes two components, namely the data Acquisition, Processing, and Distribution Component (APDC), and the Virtual Representation Component (VRC), which is developed using the Unity real-time development platform. The DTE accurately models the CNC machine’s behavior under various operating conditions. The DTE is verified and validated through testing and experimentation, particularly focusing on response time, sequencing accuracy, and seamless integration. The DTE offers two modes of operation: the online mode allows for real-time process replication, ideal for process monitoring and remote management, while the offline mode supports asynchronous G-code simulation, enabling what-if analysis and exploration of process variables. In this work, we utilize the offline mode for predictive collision detection. Using the DTE, stakeholders could make informed decisions, optimize machining processes, and ensure cost-effective operations. This research contributes to the advancement of Digital Twin technology, particularly in CNC machining, fostering innovation in the manufacturing sector and promoting the development of practical, efficient, and reliable cyber–physical industrial systems, enhancing operational capabilities and productivity.