The Digital Twin: Realizing the Cyber-Physical Production System for Industry 4.0☆

By T. Uhlemann, Christian Lehmann, R. Steinhilper. 2017

863 citations recorded by the source corpus.

Abstract Concerning current approaches to planning of manufacturing processes, the acquisition of a sufficient data basis of the relevant process information and subsequent development of feasible layout options requires 74% of the overall time-consumption. However, the application of fully automated techniques within planning processes is not yet common practice. Deficits are to be observed in the course of the use of a fully automated data acquisition of the underlying process data, a key element of Industry 4.0, as well as the evaluation and quantification and analysis of the gathered data. As the majority of the planning operations are conducted manually, the lack of any theoretical evaluation renders a benchmarking of the results difficult. Current planning processes analyze the manually achieved results with the aid of simulation. Evaluation and quantification of the planning procedure are limited by complexity that defies manual controllability. Research is therefore required with regard to automated data acquisition and selection, as the near real-time evaluation and analysis of a highly complex production systems relies on a real-time generated database. The paper presents practically feasible approaches to a multi-modal data acquisition approach, its requirements and limitations. The further concept of the Digital Twin for a production process enables a coupling of the production system with its digital equivalent as a base for an optimization with a minimized delay between the time of data acquisition and the creation of the Digital Twin. Therefore a digital data acquisition approach is necessary. As a consequence a cyber-physical production system can be generated, that opens up powerful applications. To ensure a maximum concordance of the cyber-physical process with its real-life model a multimodal data acquisition and evaluation has to be conducted. The paper therefore presents a concept for the composition of a database and proposes guidelines for the implementation of the Digital Twin in production systems in small and medium-sized enterprises.

Subfields: Industrial and Manufacturing Engineering

View Paper: https://www.semanticscholar.org/paper/845179308483d232cacf093f07d300cdfa061173

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In Industrial and Manufacturing Engineering

Citations in the corpus

Human–Cyber–Physical Systems (HCPSs) in the Context of New-Generation Intelligent Manufacturing (2019, background): “Intelligent production will realize high-quality, flexible, efficient, and sustainable product manufacturing by comprehensively enhancing production and management innovation via digital, networked, and intelligent methods [75,77].”
Impacts of Industry 4.0 technologies on Lean principles (2019, background): “…root cause of problem Waste reduction Genshi Genbutsu 5 Why’s Eyes for waste Simulation Ellgass et al. (2018); Kitazawa et al. (2016); Uhlemann, Lehmann, and Steinhilper (2017); Zhuang, Liu, and Xiong (2018) System integration Sony (2018) Internet of Things Brintrup, Ranasinghe,…”
Digital Twins and Cyber–Physical Systems toward Smart Manufacturing and Industry 4.0: Correlation and Comparison (2019, background): “Recently, DTs have also been adopted in various industries for purposes including product design [20], production line design [21], DT shop floors [22], production process optimization [23], and prognosis and health management [24].”
Reducing Offloading Latency for Digital Twin Edge Networks in 6G (2020, neutral): “DTshavebeenappliedtobridgethegapbetweenphysical anddigitalworlds[25].”
Digital twin paradigm: A systematic literature review (2021, background): “The data layer includes all different types of data previously defined in the physical layer (Uhlemann et al., 2017).”
Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, background): “[47] T.”
Low-Latency Federated Learning and Blockchain for Edge Association in Digital Twin Empowered 6G Networks (2020, background): “Despite such enabling technologies and recent developments, the gap between the outcome of data analysis and the true reflection of the status of their physical systems remains a major hurdle in designing robust control algorithms for physical systems [5].”
Construction with digital twin information systems (2020, background): “Digital twins are considered by some to represent a step in the evolution of manufacturing, capable of facilitating the implementation of Industry 4.0 principles (Rosen et al., 2015; Uhlemann et al., 2017).”
Digital Twin in Industry: State-of-the-Art (2019, background): “DTs had certain advantages, over the value stream mapping, in production optimization [41].”