How to model and implement connections between physical and virtual models for digital twin application

Jiang, Haifan; Qin, Shengfeng; Fu, Jianlin; Zhang, Jian; Ding, Guofu

doi:10.1016/j.jmsy.2020.05.012

Cited by 149 publications

(54 citation statements)

References 34 publications

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“…Consequently, the virtual models reduce the necessity of testing in the physical world with its associated costs. However, to ensure continuous improvement, some DTs are defined as a virtual model with the need of feedback of physical processed data [ 49 ]. Even so, correct modelling of the CPS and a dynamic virtualisation of the environment yield good results without the necessity for real data beforehand.…”

Section: Digital Twin From Industry 40 Conceptionmentioning

confidence: 99%

See 1 more Smart Citation

Digital Twin for Automatic Transportation in Industry 4.0

Martínez-Gutiérrez

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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Industry 4.0 is the fourth industrial revolution consisting of the digitalization of processes facilitating an incremental value chain. Smart Manufacturing (SM) is one of the branches of the Industry 4.0 regarding logistics, visual inspection of pieces, optimal organization of processes, machine sensorization, real-time data adquisition and treatment and virtualization of industrial activities. Among these tecniques, Digital Twin (DT) is attracting the research interest of the scientific community in the last few years due to the cost reduction through the simulation of the dynamic behaviour of the industrial plant predicting potential problems in the SM paradigm. In this paper, we propose a new DT design concept based on external service for the transportation of the Automatic Guided Vehicles (AGVs) which are being recently introduced for the Material Requirement Planning satisfaction in the collaborative industrial plant. We have performed real experimentation in two different scenarios through the definition of an Industrial Ethernet platform for the real validation of the DT results obtained. Results show the correlation between the virtual and real experiments carried out in the two scenarios defined in this paper with an accuracy of 97.95% and 98.82% in the total time of the missions analysed in the DT. Therefore, these results validate the model created for the AGV navigation, thus fulfilling the objectives of this paper.

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Section: Digital Twin From Industry 40 Conceptionmentioning

confidence: 99%

“…The analysis of the deviations between the results obtained and the real ones will allow for continuous improvement [ 49 ]. The fact of obtaining results without the need to have real data beforehand is a considerable advancement in the planning of new industrial processes in this paper.…”

Section: Digital Twin From Industry 40 Conceptionmentioning

confidence: 99%

Digital Twin for Automatic Transportation in Industry 4.0

Martínez-Gutiérrez

Díez-González

Ferrero-Guillén

et al. 2021

Sensors

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“…DT has been listed as one of the top 10 technology trends in 2018 [41]. It provides a means of connecting information such as real-time status of the physical product and its behaviour in the physical world with a digital representation, enabling companies or users to have a real-time view of the product and its usage in the physical space thus to adopt evidence-based actions on physical asset management [42].…”

Section: Associated Information In Traditional In-house Design Environmentmentioning

confidence: 99%

Product design Lifecycle information model (PDLIM)

Xiaojing

Wang

Qin

2021

Preprint

Self Cite

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In the era of Industry 4.0, in response to rapidly changing market and customer needs, product design and development (PDD) is evolving into a human-centred and data-driven design paradigm. The design environment gets more open often involving crowdsourcing and the design process becomes more complex, considering product family design along product whole lifecycle development, and needing more data support. Therefore, it is critical to effectively capture, share, and manage design-related information in such a complex design environment. From this perspective, it is a prerequisite to have a proper product design lifecycle information model (PDLIM) to guide information gathering, sharing and managing. To the best of our knowledge, currently, there lacks such a PDLIM to support effective product design and development (PDD), though digital twin (DT) technology shows a great potential of supporting product lifecycle information collection and management. In this paper, the overall structure of the proposed PDLIM is firstly developed to frame in all main product lifecycle stages and the corresponding key phases for structurally capturing and storing necessary data along a product lifecycle. Secondly, key design information items against the main product lifecycle stages and their corresponding key phases are explored from literature reviews and case study analyses. Thirdly, the necessity of the identified information items in the PDLIM is qualitatively evaluated by two case studies. Finally, the PDLIM is further evaluated by applying formal object-role modelling (ORM) to demonstrate how design information items are used and interacted in exemplary design interaction scenarios, and to approve that it can be formally described and managed as an information model. The evaluation results show that the PDLIM is feasible to be adapted in a crowdsourcing-combined PDD process for supporting design management, reviewing, quality control, and next round product redesign and improvement.

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“…In recent decades, with the progress of optimization algorithm and computational technology, the optimized model of the workshop manufacturing system has been developed rapidly. In view of establishment of the intelligent system in the layout, lifecycle, scheduling and other aspects of the manufacturing workshop, scientific researchers put forward the concept of digital twin [5][6][7] . The functional system of digital twin with physical entity and twin model can achieve local dynamic optimization, which makes a tremendous impact in driving the intelligent management and production of the manufacturing workshop [5][6][7][8] .…”

Section: Introductionmentioning

confidence: 99%

“…In view of establishment of the intelligent system in the layout, lifecycle, scheduling and other aspects of the manufacturing workshop, scientific researchers put forward the concept of digital twin [5][6][7] . The functional system of digital twin with physical entity and twin model can achieve local dynamic optimization, which makes a tremendous impact in driving the intelligent management and production of the manufacturing workshop [5][6][7][8] . Therefore, utilization of digital twin can solve the critical problems of intelligent model from the workshop manufacturing system [9] .…”

Section: Introductionmentioning

confidence: 99%

Construction of intelligent integrated model framework for the workshop manufacturing system via digital twin

Zhang

Zhu

Zhang

et al. 2021

Int J Adv Manuf Technol

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With the drastic development of the globally advanced manufacturing industry, transition of the original production pattern from traditional industries to advanced intelligence is completed with the least delay possible, which are still facing new challenges. Because the timeliness, stability and reliability of them is significantly restricted due to lack of the real-time communication. Therefore, an intelligent workshop manufacturing system model framework based on digital twin is proposed in this paper, driving the deep inform integration among the physical entity, data collection, and information decision-making. The conceptual and obscure of the traditional digital twin is refined, optimized, and upgraded on the basis of the four-dimension collaborative model thinking. A refined nine-layer intelligent digital twin model framework is established. Firstly, the physical evaluation is refined into entity layer, auxiliary layer and interface layer, scientifically managing the physical resources as well as the operation and maintenance of the instrument, and coordinating the overall system. Secondly, dividing the data evaluation into the data layer and the processing layer can greatly improve the flexible response-ability and ensure the synchronization of the real-time data. Finally, the system evaluation is subdivided into information layer, algorithm layer, scheduling layer, and functional layer, developing flexible manufacturing plan more reasonably, shortening production cycle, and reducing logistics cost. Simultaneously, combining SLP and artificial bee colony are applied to investigate the production system optimization of the textile workshop. The results indicate that the production efficiency of the optimized production system is increased by 34.46%.

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How to model and implement connections between physical and virtual models for digital twin application

Cited by 149 publications

References 34 publications

Digital Twin for Automatic Transportation in Industry 4.0

Digital Twin for Automatic Transportation in Industry 4.0

Product design Lifecycle information model (PDLIM)

Construction of intelligent integrated model framework for the workshop manufacturing system via digital twin

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