A game engine based digital twin framework enabling next-gen manufacturing

Research into digital twins technology for automation and infrastructure is a new and prosperous field that is at the cutting-edge of current technology. Big data analytics, cloud computing, augmented reality and Internet of Things (IoT) have been around for a couple of years, and digital twin technology is what ties it all together. The aim for this research is to create a digital twin framework without ties to professional proprietary software. This should create a modular base for anyone to build a digital twin onto quickly, designed and developed on the Cyber-Physical System (CPS). Can digital twin technology be applied to monitoring and automation in a smart factory? Is it possible to apply a live tracking algorithm to a multi-stage smart factory? Is it possible to do this in a framework format to be applicable to other facilities? Use of the Festo CPS at Middlesex University will be a case study. The journey to realising these goals starts at forming a digital representation of a physical system, incorporating live tracking between the systems, offline simulation and overall being in a modular framework structure. This allows the system to be applied to multiple case studies. The outcome of this project was to not necessarily create a faster system, but to create a system less likely to have unscheduled downtime. The live tracking algorithm developed was successful in tracking multiple carriers, however, less successful when running into certain edge cases. Simulation mode works well and outputs data of the same shape as the physical CPS.