Digital twin (DT) is one of the most promising enabling technologies for realizing smart manufacturing and Industry 4.0. DTs are characterized by the seamless integration between the cyber and physical spaces. The importance of DTs is increasingly recognized by both academia and industry. It has been almost 15 years since the concept of the DT was initially proposed. To date, many DT applications have been successfully implemented in different industries, including product design, production, prognostics and health management, and some other fields. However, at present, no paper has focused on the review of DT applications in industry. In an effort to understand the development and application of DTs in industry, this paper thoroughly reviews the state-of-the-art of the DT research concerning the key components of DTs, the current development of DTs, and the major DT applications in industry. This paper also outlines the current challenges and some possible directions for future work.

Digital Twin in Industry: State-of-the-Art

Digital Twin in manufacturing: A categorical literature review and classification

https://re.public.polimi.it/bitstream/11311/1049863/2/Negri%20et%20al%202017%20FAIM.pdf

A Review of the Roles of Digital Twin in CPS-based Production Systems

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

While there has been a recent growth of interest in the Digital Twin, a variety of definitions employed across industry and academia remain. There is a need to consolidate research such to maintain a common understanding of the topic and ensure future research efforts are to be based on solid foundations. Through a systematic literature review and a thematic analysis of 92 Digital Twin publications from the last ten years, this paper provides a characterisation of the Digital Twin, identification of gaps in knowledge, and required areas of future research. In characterising the Digital Twin, the state of the concept, key terminology, and associated processes are identified, discussed, and consolidated to produce 13 characteristics (Physical Entity/Twin; Virtual Entity/Twin; Physical Environment; Virtual Environment; State; Realisation; Metrology; Twinning; Twinning Rate; Physical-to-Virtual Connection/Twinning; Virtual-to-Physical Connection/Twinning; Physical Processes; and Virtual Processes) and a complete framework of the Digital Twin and its process of operation. Following this characterisation, seven knowledge gaps and topics for future research focus are identified: Perceived Benefits; Digital Twin across the Product Life-Cycle; Use-Cases; Technical Implementations; Levels of Fidelity; Data Ownership; and Integration between Virtual Entities; each of which are required to realise the Digital Twin.

https://research-information.bris.ac.uk/files/229965388/Characterising_The_Digital_Twin.pdf

Characterising the Digital Twin: A systematic literature review

About The Importance of Autonomy and Digital Twins for the Future of Manufacturing

A programmable logic controller comprises a processor, a PLC operating environment, a device memory, and an app container. The PLC operating environment is configured to execute a controller automation program providing a plurality of skill functions. The device memory comprises a plurality of apps which are configured to perform a discrete set of automation functions using the plurality of skill functions. The app container is logically separate from the PLC operating environment and configured to provide a runtime environment for the apps.

Extending a Programmable Logic Controller with Apps

A method for rolling-back software applications on a computing device includes the computing device storing a first version of a software application in a local application repository. The computing device retrieves a second version of the software application from a marketplace server and executes the second version of the software application in a software application runtime environment. A roll-back instruction corresponding to the second version of the software application is received by the computing device and the first version of the software application is retrieved from the local application repository. The computing device replaces the second version of the software application with the first version of the software application in the software application runtime environment.

Kurt Dirk Bettenhausen

Papers

About The Importance of Autonomy and Digital Twins for the Future of Manufacturing

Extending a Programmable Logic Controller with Apps

Rolling back apps to previously installed versions

Extending the functionality of a programmable logic controller (PLC) with apps without changing the PLC programming