Digital twin – Proof of concept

By S. Haag, R. Anderl. 2018

476 citations recorded by the source corpus.

Subfields: Industrial and Manufacturing Engineering, Safety, Risk, Reliability and Quality

View Paper: https://www.semanticscholar.org/paper/abbdcae9ae10723b9452c2fc0edfa1735750eb9a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Industrial and Manufacturing Engineering

1. Samad M. E. Sepasgozar
2. Andrew Yeh-Ching Nee
3. Hervé Panetto
4. Mario Lezoche
5. Michele Dassisti
6. Concetta Semeraro
7. Fei Tao
8. Ioannis N. Athanasiadis

In Safety, Risk, Reliability and Quality

1. Andrew Yeh-Ching Nee
2. Zhen Hu
3. Sankaran Mahadevan
4. Kai Ding
5. Chuang Wang
6. Zhi Li
7. He Zhang
8. Guanghui Zhou

Citations in the corpus

• Introducing digital twins to agriculture (2021, background): “…been useful for converging the physical and virtual spaces (Tao et al., 2018), guaranteeing information continuity through the system lifecycle (Haag and Anderl, 2018), system development and validation through simulation (Boschert and Rosen, 2016), and preventing undesirable system states…”
• Digital twin paradigm: A systematic literature review (2021, background): “…al., 2013), (Brenner and Hummel, 2017), (Schluse et al., 2017), (Stark et al., 2017), (Weber et al., 2017) (Yun et al., 2017), (Lee and Kim, 2018), (Haag and Anderl, 2018), (Leng et al., 2019) involve a comprehensive representation of all data, information, and knowledge of the physical twin in…”
• Differentiating Digital Twin from Digital Shadow: Elucidating a Paradigm Shift to Expedite a Smart, Sustainable Built Environment (2021, background): “Considering the context of the investigation, a DT can be named as a technology [23–26], system [27–31], concept [28,32–35], innovation [36,37], or paradigm [38–41].”
• Digital Twin in the IoT Context: A Survey on Technical Features, Scenarios, and Architectural Models (2020, background): “There are ongoing technological trends for creating DTs in several fields: smart cities [138], [196] or specific issues of the cities, like traffic [197], aerospace [187], aircraft fleet management [198], innovative factory [2], [199]–[201], environmental management [202], logistics [203], [204], th”
• Knowledge-driven digital twin manufacturing cell towards intelligent manufacturing (2019, background): “The fusion of current manufacturing technologies with new generation information technologies is guiding another paradigm shift in manufacturing, generally known as the fourth industrial revolution (Haag and Anderl 2018).”
• Material and energy flows of the iron and steel industry: Status quo, challenges and perspectives (2020, background): “As a future direction, “digital twins” [140] of these real-life physical objects are able to be built, as shown in Fig.”
• A comprehensive review of digital twin — part 1: modeling and twinning enabling technologies (2022, methodology): “As an initial digital twin proof of concept, Haag and Anderl (2018) demonstrated simultaneous simulation and measurement of forces in a beam bending test bench.”
• Digital Twin in Industry: State-of-the-Art (2019, methodology): “DT of a bending test bench, together with some specific modeling methods of a physical entity, digital entity, and connection [21].”
• A Review on Digital Twin Technology in Smart Grid, Transportation System and Smart City: Challenges and Future (2023, background): “In [8], the application of DT in the smart automotive industry is reviewed.”