Digital Twins:State of the Art Theory and Practice, Challenges, and Open Research Questions

Digital Twin was introduced over a decade ago, as an innovative all-encompassing tool, with perceived benefits including real-time monitoring, simulation and forecasting. However, the theoretical framework and practical implementations of digital twins (DT) are still far from this vision. Although successful implementations exist, sufficient implementation details are not publicly available, therefore it is difficult to assess their effectiveness, draw comparisons and jointly advance the DT methodology. This work explores the various DT features and current approaches, the shortcomings and reasons behind the delay in the implementation and adoption of digital twin. Advancements in machine learning, internet of things and big data have contributed hugely to the improvements in DT with regards to its real-time monitoring and forecasting properties. Despite this progress and individual company-based efforts, certain research gaps exist in the field, which have caused delay in the widespread adoption of this concept. We reviewed relevant works and identified that the major reasons for this delay are the lack of a universal reference framework, domain dependence, security concerns of shared data, reliance of digital twin on other technologies, and lack of quantitative metrics. We define the necessary components of a digital twin required for a universal reference framework, which also validate its uniqueness as a concept compared to similar concepts like simulation, autonomous systems, etc. This work further assesses the digital twin applications in different domains and the current state of machine learning and big data in it. It thus answers and identifies novel research questions, both of which will help to better understand and advance the theory and practice of digital twins

Digital twin model of two-arm collaborative robot for human arms motion simulation using reverse engineering

This study proposes a digital twin (DT) model for a two-arm collaborative robot that can be deployed to simulate human arm motions using the reverse engineering process. A collaborative robot named ABB Yumi – IRB14000 was considered for this study. The purpose of the experiment was to find the best version of the digital twin model by applying translation and rotation constraints in every part of the CAD model of the robot. After adding features to the robot part files, Virtual Reality Modeling Language (VRML) format files were being created to assemble it in 3D world Editor for DT formation and a grid layout was created that contained the control panel of the collaborative model digital twin to connect it with the real world. Finally, a cyber-physical system (CPS) interface was built to replicate human motion. Deep reinforcement learning will be implemented using these two models for human motion simulation

Enhancing digital twins through reinforcement learning

Digital Twins are core enablers of smart and autonomous manufacturing systems. Although they strive to represent their physical counterpart as accurately as possible, slight model or data errors will remain. We present an algorithm to compensate for those residual errors through Reinforcement Learning (RL) and data fed back from the manufacturing system. When learning, the Digital Twin acts as teacher and safety policy to ensure minimal performance. We test the algorithm in a sheet metal assembly context, in which locators of the fixture are optimally adjusted for individual assemblies. Our results show a fast adaption and improved performance of the autonomous system