Nowadays, along with the application of new-generation information technologies in industry and manufacturing, the big data-driven manufacturing era is coming. However, although various big data in the entire product lifecycle, including product design, manufacturing, and service, can be obtained, it can be found that the current research on product lifecycle data mainly focuses on physical products rather than virtual models. Besides, due to the lack of convergence between product physical and virtual space, the data in product lifecycle is isolated, fragmented, and stagnant, which is useless for manufacturing enterprises. These problems lead to low level of efficiency, intelligence, sustainability in product design, manufacturing, and service phases. However, physical product data, virtual product data, and connected data that tie physical and virtual product are needed to support product design, manufacturing, and service. Therefore, how to generate and use converged cyber-physical data to better serve product lifecycle, so as to drive product design, manufacturing, and service to be more efficient, smart, and sustainable, is emphasized and investigated based on our previous study on big data in product lifecycle management. In this paper, a new method for product design, manufacturing, and service driven by digital twin is proposed. The detailed application methods and frameworks of digital twin-driven product design, manufacturing, and service are investigated. Furthermore, three cases are given to illustrate the future applications of digital twin in the three phases of a product respectively.

Digital twin-driven product design, manufacturing and service with big data

Digital Twin in manufacturing: A categorical literature review and classification

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

https://hal.archives-ouvertes.fr/hal-01513846/document

Shaping the digital twin for design and production engineering

Review of digital twin about concepts, technologies, and industrial applications

The vision of the Digital Twin itself refers to a comprehensive physical and functional description of a component, product or system, which includes more or less all information which could be useful in all—the current and subsequent—lifecycle phases. In this chapter we focus on the simulation aspects of the Digital Twin. Today, modelling and simulation is a standard process in system development, e.g. to support design tasks or to validate system properties. During operation and for service first simulation-based solutions are realized for optimized operations and failure prediction. In this sense, simulation merges the physical and virtual world in all life cycle phases. Current practice already enables the users (designer, SW/HW developers, test engineers, operators, maintenance personnel, etc) to master the complexity of mechatronic systems.

Digital Twin—The Simulation Aspect

Der seit einigen Jahren vielfaltig verwendete Begriff Digital Twin beinhaltet nach wie vor visionare Aspekte. Deren Berucksichtigung wird zur Entwicklung einer nachsten Generation digitaler Zwillinge fuhren. In der in diesem Beitrag beschriebenen Vision werden die Lebenszyklus-ubergreifenden Perspektiven vorgestellt und anhand von zwei Beispielen diskutiert. Neben seinem Einsatz in der Entwicklung wird der digitale Zwilling als Abbild des realen Systems im Betrieb und Service zunehmend neue simulationsbasierte Losungen ermoglichen. Die Lebenszyklus-ubergreifende Perspektive gepaart mit der Veranderung der Wertschopfungsketten wird zu einem Digital-Twin-Okosystem fuhren.

Next Generation Digital Twin: Öko-System für simulationsbasierte Lösungen im Betrieb

Next Generation Digital Twin: an Ecosystem for Mechatronic Systems?

CAD-Based Automated Assembly Planning for Variable Products in Modular Production Systems☆

Method for generating boundary conditions for at least one model for the simulation of at least one civil infrastructure, said method comprising the process steps: (a) mapping of spatially distributed installations connected to the at least one civil infrastructure onto a data structure; (b) typification of the spatially distributed installations; and (c) determination of boundary conditions for the at least one model by means of the spatially distributed installations that have been typified.

Stefan Boschert

Papers

Digital Twin—The Simulation Aspect

Next Generation Digital Twin: Öko-System für simulationsbasierte Lösungen im Betrieb

Next Generation Digital Twin: an Ecosystem for Mechatronic Systems?

CAD-Based Automated Assembly Planning for Variable Products in Modular Production Systems☆

Simulation of civil infrastructure