Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

Sierla, Seppo; Azangoo, Mohammad; Fay, Alexander; Vyatkin, Valeriy; Papakonstantinou, Nikolaos

doi:10.1109/isie45063.2020.9152371

Cited by 20 publications

(10 citation statements)

References 32 publications

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“…Traditionally, digital twins are defined in a domain-specific way that relies on a mathematical-physical description resulting from specific spatial shapes, materials, and physical phenomena/equations, see for example machining processes for high-tech/aerospace industry [15,16]. To simplify the design process of digital twins, generation of digital twins from 2D and 3D CAD plans/models is addressed in [17], by means of generating graphs from available plans/models that pose a basis for graph matchmaking. This approach has been slightly improved, generalized, and matured in [18], targeting generation of digital twins for brown-fields production systems in general, yet considering piping and instrumentation diagrams still as a use-case.…”

Section: Digital Twins For Production Systemsmentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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Smart production systems conforming the Industry 4.0 vision are based on subsystems that are integrated in a way that supports high flexibility and re-configurability. Specific components and devices, such as industrial and mobile robots or transport systems, now pose full-blown systems, and the entire Industry 4.0 production system constitutes a system-of-systems. Testing, fine-tuning, and production planning are important tasks in the entire engineering production system life-cycle. All these steps can be significantly supported and improved by digital twins, which are digitalized replicas of physical systems that are synchronized with the real systems at runtime. However, the design and implementation of digital twins for such integrated, yet partly stand-alone, industrial sub-systems can represent challenging and significantly time-consuming engineering tasks. In this article, the problem of the digital twin design for discrete-event production systems is addressed. The article also proposes to utilize a formal description of production resources and related production operations that the resources can perform. An executable version of such formalization can be automatically derived into a form of a digital twin. Such a derived digital twin can be enhanced with operation duration times that are obtained with process mining methods, leading to more realistic simulations for the entire production system. The proposed solution was successfully tested and validated in the Industry 4.0 Testbed, equipped with four robots and a transport system, which is utilized as a use-case in this article.

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Section: Digital Twins For Production Systemsmentioning

confidence: 99%

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Novák

Vyskočil

2022

Processes

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“…Digital twins for brownfield processes can be automatically extracted from available or extractable information such as 3D scan of production sites [32,33], 3D models [35], P&ID documents [36], design phase requirements [37], archived data repository [38] and mixture of these information [39]. Sierla et al [40] extracted graph models of process plants from two different sources, 3D CAD models and P&IDs, for the generation of digital twins. However, the extracted graph models were at varying levels of abstraction, making it difficult to compare them for validation purposes.…”

Section: Automatic Generation Of Digital Twinsmentioning

confidence: 99%

A Methodology for Generating a Digital Twin for Process Industry: A Case Study of a Fiber Processing Pilot Plant

et al. 2022

Self Cite

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Digital twins are now one of the top trends in Industry 4.0, and many companies are using them to increase their level of digitalization, and, as a result, their productivity and reliability. However, the development of digital twins is difficult, expensive, and time consuming. This article proposes a semiautomated methodology to generate digital twins for process plants by extracting process data from engineering documents using text and image processing techniques. The extracted information is used to build an intermediate graph model, which serves as a starting point for generating a model in a simulation software. The translation of a graph-based model into a simulation software environment necessitates the use of simulator-specific mapping rules. This paper describes an approach for generating a digital twin based on a steady state simulation model, using a Piping and Instrumentation Diagram (P&ID) as the main source of information. The steady state modeling paradigm is especially suitable for use cases involving retrofits for an operational process plant, also known as a brownfield plant. A methodology and toolchain is proposed, consisting of manual, semi-automated and fully automated steps. A pilot scale brownfield fiber processing plant was used as a case study to demonstrate our proposed methodology and toolchain, and to identify and address issues that may not occur in laboratory scale case studies. The article concludes with an evaluation of unresolved concerns and future research topics for the automated development of a digital twin for a brownfield process system.

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“…The first one involves the use of heuristics to detect certain well-known shapes, such as geometrical symbols, arrows, connectors, tables and even text [3], [4], [5], [6]. The second and most recent one relies on deep learning techniques in which the algorithms are trained to recognise shapes based on the collection and tagging of numerous samples [7], [8], [9], [10]. Both approaches have advantages and disadvantages depending on the use case.…”

Section: Related Workmentioning

confidence: 99%

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

Toral

Moreno‐García

Elyan

et al. 2021

Lecture Notes in Computer Science

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Corrosion circuit mark up in engineering drawings is one of the most crucial tasks performed by engineers. This process is currently done manually, which can result in errors and misinterpretations depending on the person assigned for the task. In this paper, we present a semi-automated framework which allows users to upload an undigitised Piping and Instrumentation Diagram, i.e. without any metadata, so that two key shapes, namely pipe specifications and connection points, can be localised using deep learning. Afterwards, a heuristic process is applied to obtain the text, orient it and read it with minimal error rates. Finally, a user interface allows the engineer to mark up the corrosion sections based on these findings. Experimental validation shows promising accuracy rates on finding the two shapes of interest and enhance the functionality of optical character recognition when reading the text of interest.

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Integrating 2D and 3D Digital Plant Information Towards Automatic Generation of Digital Twins

Cited by 20 publications

References 32 publications

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

Digitalized Automation Engineering of Industry 4.0 Production Systems and Their Tight Cooperation with Digital Twins

A Methodology for Generating a Digital Twin for Process Industry: A Case Study of a Fiber Processing Pilot Plant

A Deep Learning Digitisation Framework to Mark up Corrosion Circuits in Piping and Instrumentation Diagrams

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