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

Uhlemann, Thomas H.-J.; Lehmann, Christian W.; Steinhilper, Rolf

doi:10.1016/j.procir.2016.11.152

Cited by 774 publications

(338 citation statements)

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“…The automatic generation of the virtual factory model is intended to go beyond the previous efforts involving automatic generation of single level factory simulations by generating a multi-resolution model and using standard formats of input files. Uhlemann et al (2017) present a concept for the realization of a Digital Twin of the production system within SMEs. Their concept is feasible by assuring sufficient data quality with minimized investment costs, and without compromising the advantages of the Digital Twin and of the CPPS.…”

Section: Resultsmentioning

confidence: 99%

“…As the database of production data in SME is extremely heterogeneous and its quality regularly insufficient for the realization of the Digital Twin, the authors (Uhlemann, Lehmann, & Steinhilper, 2017) introduce sensor based tracking and machine vision for manufacturing process data acquisition. Sensor-based tracking provides information regarding routes and position of production employees and routes and position of large and highly mobile production devices, e.g.…”

Section: Resultsmentioning

confidence: 99%

“…Authors (Uhlemann, Lehmann, & Steinhilper, 2017) propose the next step as the development of a virtual production system, which generates data following the real production system with the two implemented data acquisition technologies. Based upon this, the data layer and the information and optimization section are constructed and verified trough testing.…”

Section: Resultsmentioning

confidence: 99%

“…However, methods of Industry 4.0 are currently still under-represented within manufacturing operations. Authors furthermore describe the following difficulties in the course of the realization of the Digital Twin as an essential precondition of a CPPS (Uhlemann, Lehmann, & Steinhilper, 2017):…”

Section: Problem Descriptionmentioning

confidence: 99%

“…A comprehensive image of the reality within a production system therefore can only be achieved through a multi-modal data acquisition, similarly to the procedures in modern self-driving automobiles. (Uhlemann, Lehmann, & Steinhilper, 2017) …”

Section: Problem Descriptionmentioning

confidence: 99%

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Industry 4.0 and the New Simulation Modelling Paradigm

Rodič¹

2017

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Background and Purpose:The aim of this paper is to present the influence of Industry 4.0 on the development of the new simulation modelling paradigm, embodied by the Digital Twin concept, and examine the adoption of the new paradigm via a multiple case study involving real-life R&D cases involving academia and industry. Design: We introduce the Industry 4.0 paradigm, presents its background, current state of development and its influence on the development of the simulation modelling paradigm. Further, we present the multiple case study methodology and examine several research and development projects involving automated industrial process modelling, presented in recent scientific publications and conclude with lessons learned. Results: We present the research problems and main results from five individual cases of adoption of the new simulation modelling paradigm. Main lesson learned is that while the new simulation modelling paradigm is being adopted by big companies and SMEs, there are significant differences depending on company size in problems that they face, and the methodologies and technologies they use to overcome the issues. Conclusion: While the examined cases indicate the acceptance of the new simulation modelling paradigm in the industrial and scientific communities, its adoption in academic environment requires close cooperation with industry partners and diversification of knowledge of researchers in order to build integrated, multi-level models of cyber-physical systems. As shown by the presented cases, lack of tools is not a problem, as the current generation of general purpose simulation modelling tools offers adequate integration options.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

Section: Problem Descriptionmentioning

confidence: 99%

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