Automatic generation of digital twin industrial system from a high level specification

Campos, Julio Garrido; López, Juan Sáez; Quiroga, José Ignacio Armesto; Seoane, Angel Manuel Espada

doi:10.1016/j.promfg.2020.01.197

Cited by 16 publications

(10 citation statements)

References 6 publications

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“…In general, no consensus is reached in terms of the standards of representation. In terms of automation systems, example tools include point cloud synthesis, tomography, and ultrasonic testing [4].…”

Section: Characterizing the Digital Twin Techniquementioning

confidence: 99%

“…As a simple example, let us take a close look at how a robot joint (a mechanical part) is created and made ready for delivery to the customers under the current manufacturing industrial practice. At the beginning, the customer proposes the expected functionalities and the specifications that must be met, including, for instance, the acceptable range of the size and weight of the mechanical part, the different types of forces to withstand and the length of the service life [4][5][6]. Then, the designers make efforts to meet these demands using their domain knowledge and, with the aid of CAD and CAE tools, make an analysis of their design and run simulations.…”

Section: Introductionmentioning

confidence: 99%

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Industrial applications of digital twins

Jiang

Yin

et al. 2021

Phil. Trans. R. Soc. A.

179

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A digital twin (DT) is classically defined as the virtual replica of a real-world product, system, being, communities, even cities that are continuously updated with data from its physical counterpart, as well as its environment. It bridges the virtual cyberspace with the physical entities and, as such, is considered to be the pillar of Industry 4.0 and the innovation backbone of the future. A DT is created and used throughout the whole life cycle of the entity it replicates, from cradle to grave, so to speak. This article focuses on the present state of the art of DTs, concentrating on the use of DTs in industry in the context of smart manufacturing, especially from the point of view of plantwide optimization. The main capabilities of DTs (mirroring, shadowing and threading) are discussed in this context. The article concludes with a perspective on the future. This article is part of the theme issue ‘Towards symbiotic autonomous systems’.

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Section: Characterizing the Digital Twin Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Industrial applications of digital twins

Jiang

Yin

et al. 2021

Phil. Trans. R. Soc. A.

179

View full text Add to dashboard Cite

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“…A digital twin has been generated for hardware-in-the-loop testing of control software [52], another activity that is not applicable in the context of steady state models that does not capture time-related behavior. A digital twin has been generated for the analysis of bottlenecks [53]; out of all the works reviewed in this section, this is the only one that is relevant to the specific purpose of the research presented in this paper, which is the design and validation of retrofits. However, the bottleneck analysis was performed specifically in the context of discrete manufacturing systems, so the approach is not applicable to the continuous processes addressed in this paper.…”

Section: Automatic Generation Of Digital Twinsmentioning

confidence: 99%

“…The required information for simulation model creation can be extracted from these documents. The source information for the digital twin creation is not limited to design and engineering documents; for example, in [53] it was shown that a low fidelity digital twin has been generated automatically from high level requirements of the initial design phase of the project. Ref.…”

Section: Automatic Generation Of Digital Twinsmentioning

confidence: 99%

Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant

et al. 2020

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Researchers have proposed various models for assessing design alternatives for process plant retrofits. Due to the considerable engineering effort involved, no such models exist for the great majority of brownfield process plants, which have been in operation for years or decades. This article proposes a semi-automatic methodology for generating a digital twin of a brownfield plant. The methodology consists of: (1) extracting information from piping and instrumentation diagrams, (2) converting the information to a graph format, (3) applying graph algorithms to preprocess the graph, (4) generating a simulation model from the graph, (5) performing manual expert editing of the generated model, (6) configuring the calculations done by simulation model elements and (7) parameterizing the simulation model according to recent process measurements in order to obtain a digital twin. Since previous work exists for steps (1–2), this article focuses on defining the methodology for (3–5) and demonstrating it on a laboratory process. A discussion is provided for (6–7). The result of the case study was that only few manual edits needed to be made to the automatically generated simulation model. The paper is concluded with an assessment of open issues and topics of further research for this 7-step methodology.

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“…Simulation models have been generated automatically from P&IDs for the purpose of testing of control software [10] and for Hardware-in-loop testing [11]. [12] generated low fidelity simulation models from high level specifications available at the preliminary design phase of the automation projects. Later these models were synchronized with the control and manufacturing operations management system to obtain a digital twin with improved fidelity.…”

Section: Related Researchmentioning

confidence: 99%

Generating an industrial process graph from 3D pipe routing information

Sierla

Azangoo

Vyatkin

2020

2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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The automatic generation of digital twins of industrial processes requires the integration of several sources of information. If the twin is expected to accurately capture thermo-hydraulic phenomena, dimensions of tanks and other process components as well as detailed pipe routing information is relevant. Such information is not comprehensively captured in P&IDs (Piping & Instrumentation Diagrams), but it is available from 3D CAD models. However, information about control loops is not available from 3D CAD models, but is available from P&IDs. Previous research has demonstrated the extraction of such information from machine-readable P&IDs and 3D CAD models and converting this information to graphs. Further research is expected on applying graph matching methods for integrating these separate graphs to a common graph-based data structure that captures all of the desired information. This common model could support further work to develop digital twins. A major obstacle to this is that the graphs that have currently been generated from P&IDs and 3D CAD models are at very different abstraction levels, so graph matching methods are not feasible. This article address this obstacle by building on previous work, in which graphs were generated from P&IDs and 3D CAD models. The contribution of this paper is several novel algorithms for preprocessing a 3D CAD generated graph, until it is at the same level of abstraction as a P&ID generated graph of the same industrial process. The algorithms are demonstrated in the context of a laboratory process.

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Automatic generation of digital twin industrial system from a high level specification

Cited by 16 publications

References 6 publications

Industrial applications of digital twins

Industrial applications of digital twins

Towards Semi-Automatic Generation of a Steady State Digital Twin of a Brownfield Process Plant

Generating an industrial process graph from 3D pipe routing information

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