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

This paper reviews the recent development of Digital Twin technologies in manufacturing systems and processes, to analyze the connotation, application scenarios, and research issues of Digital Twin-driven smart manufacturing in the context of Industry 4.0. To understand Digital Twin and its future potential in manufacturing, we summarized the definition and state-of-the-art development outcomes of Digital Twin. Existing technologies for developing a Digital Twin for smart manufacturing are reviewed under a Digital Twin reference model to systematize the development methodology for Digital Twin. Representative applications are reviewed with a focus on the alignment with the proposed reference model. Outstanding research issues of developing Digital Twins for smart manufacturing are identified at the end of the paper.

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Digital Twin-driven smart manufacturing: Connotation, reference model, applications and research issues

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

With the rapid advancement of cyber-physical systems, Digital Twin (DT) is gaining ever-increasing attention owing to its great capabilities to realize Industry 4.0. Enterprises from different fields are taking advantage of its ability to simulate real-time working conditions and perform intelligent decision-making, where a cost-effective solution can be readily delivered to meet individual stakeholder demands. As a hot topic, many approaches have been designed and implemented to date. However, most approaches today lack a comprehensive review to examine DT benefits by considering both engineering product lifecycle management and business innovation as a whole. To fill this gap, this work conducts a state-of-the art survey of DT by selecting 123 representative items together with 22 supplementary works to address those two perspectives, while considering technical aspects as a fundamental. The systematic review further identifies eight future perspectives for DT, including modular DT, modeling consistency and accuracy, incorporation of Big Data analytics in DT models, DT simulation improvements, VR integration into DT, expansion of DT domains, efficient mapping of cyber-physical data and cloud/edge computing integration. This work sets out to be a guide to the status of DT development and application in today’s academic and industrial environment.

A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives

Designing efficient sensors for soft robotics aiming at human machine interaction remains a challenge. Here, we report a smart soft-robotic gripper system based on triboelectric nanogenerator sensors to capture the continuous motion and tactile information for soft gripper. With the special distributed electrodes, the tactile sensor can perceive the contact position and area of external stimuli. The gear-based length sensor with a stretchable strip allows the continuous detection of elongation via the sequential contact of each tooth. The triboelectric sensory information collected during the operation of soft gripper is further trained by support vector machine algorithm to identify diverse objects with an accuracy of 98.1%. Demonstration of digital twin applications, which show the object identification and duplicate robotic manipulation in virtual environment according to the real-time operation of the soft-robotic gripper system, is successfully created for virtual assembly lines and unmanned warehouse applications. Designing efficient sensors for human machine interaction remains a challenge. Here, the authors present a soft robotic fingers system based on a triboelectric nanogenerator (L-TENG) sensor to capture the continuous motion of soft gripper and a soft tactile (T-TENG) sensor for tactile sensing, that can achieve an object recognition accuracy of 98.1%.

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Triboelectric nanogenerator sensors for soft robotics aiming at digital twin applications

Digital twin technology is considered as a key technology to realize cyber-physical systems (CPS) However, due to the complexity of building a digital equivalent in virtual space to its physical counterpart, very little progress has been achieved in digital twin application, especially in the complex product assembly shop-floor In this paper, we propose a framework of digital twin-based smart production management and control approach for complex product assembly shop-floors Four core techniques embodied in the framework are illustrated in detail as follows: (1) real-time acquisition, organization, and management of the physical assembly shop-floor data, (2) construction of the assembly shop-floor digital twin, (3) digital twin and big data-driven prediction of the assembly shop-floor, and (4) digital twin-based assembly shop-floor production management and control service To elaborate how to apply the proposed approach to reality, we present detailed implementation process of the proposed digital twin-based smart production management and control approach in a satellite assembly shop-floor scenario Meanwhile, the future work to completely fulfill digital twin-based smart production management and control concept for complex product assembly shop-floors are discussed

Digital twin-based smart production management and control framework for the complex product assembly shop-floor

Digital twin (DT) technology provides a novel, feasible, and clear implementation path for the realization of smart manufacturing and cyber-physical systems (CPS). Currently, DT is applied to all stages of the product lifecycle, including design, production, and service, although its application in the production stage is not yet extensive. Shop-floor digital twin (SDT) is a digital mapping model of the corresponding physical shop-floor. How to build and apply SDT has always been challenging when applying DT technology in the production phase. To address the existing problems, this paper first reviews the origin and evolution of DT, including its application status in the production stage. Then, an implementation framework for the construction and application of SDT is proposed. Three key implementation techniques are explained in detail: the five-dimensional modeling of SDT; DT-based 3D visual and real-time monitoring of shop-floor operating status; and prediction of shop-floor operating status based on SDT using Markov chain. A DT-based visual monitoring and prediction system (DT-VMPS) for shop-floor operating status is developed, and the feasibility and effectiveness of the proposed method are demonstrated through the use of an engineering case study. Finally, a summary of the contributions of the paper is given, and future research issues are discussed.

The connotation of digital twin, and the construction and application method of shop-floor digital twin

Prompt responses to problems/faults arising in an assembly workshop are crucial in terms of production reliability and efficiency. However, human-dependent tasks are time-consuming and prone to error. In this paper, we propose a knowledge discovery approach. We extract the patterns of associations between texts in problem-solving records to generate appropriate solutions automatically. First, we use an enhanced latent Dirichlet allocation (EnLDA) technique to explore the document-topic and topic-word distributions of a text corpus recording assembly problems, causes, and solutions. To increase accuracy, we adjust the elements of the document-term matrix, and we assign term frequency-inverse document frequencies. Second, we use the Refining Density-based Spatial Clustering of Application with Noise (Rf-DBSCAN) algorithm for text clustering. This refines the distances among topic distribution vectors and incorporates noise objects into clustering. This clusters textual documents with similar semantic information, maximizing information retention. Third, we use the Apriori algorithm to identify pattern associations among document clusters that represent the problems, causes, and solutions. We perform a case study using field data from an automobile assembly workshop. The results show that the method retrieves hidden but valuable information from textual records. The decision support knowledge facilitates assembly problem-solving.

Knowledge discovery using an enhanced latent Dirichlet allocation-based clustering method for solving on-site assembly problems

The invention discloses a visual workshop monitoring method, device and equipment. The visual workshop monitoring method comprises: establishing a virtual workshop three-dimensional scene model; determining a running state of the workshop three-dimensional scene model according to the real-time data of the workshop; and monitoring the workshop field in real time based on the running state of the workshop three-dimensional scene model and displaying state monitoring information. According to the embodiment of the invention, with the virtual workshop three-dimensional scene model driven by the real-time data of the workshop, the real-time mapping of the workshop working field is realized; the full process of the production activity can be monitored visually and the working field of the workshop is displayed vividly; a two-dimensional display panel is used for realizing coordinated monitoring; state information of all elements of the workshop is displayed clearly; and the visual level ofworkshop monitoring is improved. And thus a problem of service restriction caused by the non-transparent phenomenon of the workshop working and many flow links are solved effectively.

Visual workshop monitoring method, device and equipment

The invention discloses a quality control method, device and equipment for a product assembly process. The quality control method for the product assembly process comprises the following steps of constructing a digital twinning quality control model; acquiring the quality data of a physical workshop through the digital twinning quality control model; and calculating the change trend of the qualitydata at the next moment according to the state change of the quality data, and obtaining a calculation result. According to the embodiment of the invention, the digital twinning quality management and control model for the cooperative work of the physical workshop, a virtual workshop and a workshop production management system is constructed, so that the acquisition, analysis and feedback of thequality data in the assembly process are realized; the future change trend of the quality data is predicted according to the current state change of the quality data, an operator can be reminded to adjust the quality data in time, the quality data can be regulated and controlled within a reasonable range, the method, device and equipment is suitable for the discrete assembly process, and a new wayis provided for the complex product quality management and control.

Xiong Hui

Papers

Digital twin-based smart production management and control framework for the complex product assembly shop-floor

The connotation of digital twin, and the construction and application method of shop-floor digital twin

Knowledge discovery using an enhanced latent Dirichlet allocation-based clustering method for solving on-site assembly problems

Visual workshop monitoring method, device and equipment

Quality control method, device and equipment for product assembly process