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Production systems have to be adapted continuously to changing circumstances. This means that the responsible production planner has to frequently make decisions about reconfiguring complex systems under uncertainty whose outcome greatly affects the company's business success. Discrete-event simulation is one powerful tool to support the necessary analysis and scenario evaluation, but still remains time-consuming and tricky to set up and to maintain. When implemented as a digital twin of the production system, the simulation model can maintain a high degree of accuracy over a long time period. An approach to realise this potential is presented and illustrated in this paper with a use case from the automotive industry. This paper contributes several new methods and findings to the development of digital twins of production systems: Firstly, it demonstrates how exceptional events in the validation of the digital twin can be handled. Secondly, it shows how structural changes in the system can be discovered using data on machine activity and process mining. Thirdly, the paper introduces a possibility on how to assess the accuracy of the digital twin. Furthermore, it demonstrates how to assess the robustness of the digital twin to estimation errors in machine processing times. [ABSTRACT FROM AUTHOR]

This paper provides a comprehensive analysis of recent advancements in additive manufacturing, a transformative approach to industrial production that allows for the layer-by-layer construction of complex parts directly from digital models. Focusing specifically on Directed Energy Deposition, it begins by clarifying the fundamental principles of metal additive manufacturing as defined by International Organization of Standardization and American Society for Testing and Materials standards, with an emphasis on laser- and powder-based methods that are pivotal to Directed Energy Deposition. It explores the critical process mechanisms that can lead to defect formation in the manufactured parts, offering in-depth insights into the factors that influence these outcomes. Additionally, the unique mechanisms of defect formation inherent to Directed Energy Deposition are examined in detail. The review also covers the current landscape of process evaluation and non-destructive testing methods essential for quality assurance, including both traditional and contemporary in situ monitoring techniques, with a particular focus given to advanced machine-vision-based methods for geometric analysis. Furthermore, the integration of process monitoring, multiphysics simulation models, and data analytics is discussed, charting a forward-looking roadmap for the development of Digital Twins in Laser–Powder-based Directed Energy Deposition. Finally, this review highlights critical research gaps and proposes directions for future research to enhance the accuracy and efficiency of Directed Energy Deposition systems. [ABSTRACT FROM AUTHOR]

As an important enabling technology in the era of Industry 4.0, the intelligent connected vehicle (ICV) facilitates robust data interaction with the outside through sensors and communication technologies, ultimately making scientific decisions based on environmental perception information. However, due to constraints such as limited communication bandwidth and computing resources, the influx of data simultaneously impedes the sustainable optimisation of the vehicle decision making process at the same time. As a novel technology that effectively connects physical and virtual space, the special ability of the digital twin (DT) is to identify characteristics within a certain lifecycle, thereby garnering widespread attention across various industries. The purpose of this paper is to review the contribution of digital twins in the application field of intelligent vehicles and explore its potential for development. First, the key technologies of ICV provide a basis for the embedding of digital twins. Then, by analysing the development process and technical composition of digital twins, readers can better understand the concept of digital twins. Finally, the application of DTs in ICV is reviewed from the perspective of vehicles, traffic facilities, and occupants. Future challenges and opportunities in this direction are described at the same time.

Civil structural health monitoring (CSHM) tracks different aspects of an infrastructure system's service and safety condition by utilizing reliably measured data and physics-based model simulations. Data and physical models are coupled with heuristic experience to proactively represent current and expected future performance. In the past two decades, more bridges and dams have been instrumented and monitored during and after construction to determine their performances and responses to various loading, material, boundary, and environmental conditions. Furthermore, bridge and dam owners increasingly utilize civionics systems to obtain essential data for developing data-driven asset management programs and addressing the state of good repair requirements. [ABSTRACT FROM AUTHOR]