{"id":{"repo_id":"middlesex","oai_identifier":"oai:repository.mdx.ac.uk:148z1y"},"canonical_url":"https://search.dev.ndltd.org/etd/middlesex/oai:repository.mdx.ac.uk:148z1y","repository":{"repo_id":"middlesex","name":"Middlesex University","base_url":"https://repository.mdx.ac.uk/oai2"},"display":{"title":"A game engine based digital twin framework enabling next-gen manufacturing","abstract":"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. 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