Potential for combining semantics and data analysis in the context of digital twins

Vogel‐Heuser, Birgit; Ocker, Felix; Weiss, Iris R.; Mieth, Robert; Mann, Frederik

doi:10.1098/rsta.2020.0368

Cited by 20 publications

(19 citation statements)

References 61 publications

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“…They can also be regarded as models themselves, applicable across various contexts within production systems. The authors in [ 16 ] shed light on the significance of integrating expert knowledge, data analytics, and Knowledge-Based Systems (KBSs) for production systems in general and the development of DTs specifically. KBSs, like knowledge graphs, play a crucial role in the realm of DTs by offering explicit semantics, such as representations of DTs using web ontology language (OWL) [ 16 ].…”

Section: Related Workmentioning

confidence: 99%

“…The authors in [ 16 ] shed light on the significance of integrating expert knowledge, data analytics, and Knowledge-Based Systems (KBSs) for production systems in general and the development of DTs specifically. KBSs, like knowledge graphs, play a crucial role in the realm of DTs by offering explicit semantics, such as representations of DTs using web ontology language (OWL) [ 16 ]. In the present study, the knowledge graph introduces a semantic layer that allows better monitoring of the business process and enables coordination among various elements of the digital twin.…”

Section: Related Workmentioning

confidence: 99%

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Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Stavropoulou,

Tsitseklis,

Mavraidi

et al. 2024

Sensors

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In the highly competitive field of material manufacturing, stakeholders strive for the increased quality of the end products, reduced cost of operation, and the timely completion of their business processes. Digital twin (DT) technologies are considered major enablers that can be deployed to assist the development and effective provision of manufacturing processes. Additionally, knowledge graphs (KG) have emerged as efficient tools in the industrial domain and are able to efficiently represent data from various disciplines in a structured manner while also supporting advanced analytics. This paper proposes a solution that integrates a KG and DTs. Through this synergy, we aimed to develop highly autonomous and flexible DTs that utilize the semantic knowledge stored in the KG to better support advanced functionalities. The developed KG stores information about materials and their properties and details about the processes in which they are involved, following a flexible schema that is not domain specific. The DT comprises smaller Virtual Objects (VOs), each one acting as an abstraction of a single step of the Industrial Business Process (IBP), providing the necessary functionalities that simulate the corresponding real-world process. By executing appropriate queries to the KG, the DT can orchestrate the operation of the VOs and their physical counterparts and configure their parameters accordingly, in this way increasing its self-awareness. In this article, the architecture of such a solution is presented and its application in a real laser glass bending process is showcased.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Stavropoulou,

Tsitseklis,

Mavraidi

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In practice, time-annotated production data is difficult to process [4] due to numerous reasons. The system architecture of production systems is heterogeneous and consists of many different interacting systems whose components are produced by different manufacturers and may use heterogeneous data models [5].…”

Section: Introductionmentioning

confidence: 99%

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

Tom¹,

Hranisavljevic²,

Fay³

2022

Preprint

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The analysis of event data from production systems is the basis for many applications associated with Industry 4.0. However, heterogeneous and disjoint data is common in this domain. As a consequence, contextual information of an event might be incomplete or improperly interpreted which results in suboptimal analysis results. This paper proposes an approach to access a production systems' event data based on the event data's context (such as the product type, process type or process parameters). The approach extracts filtered event logs from a database system by combining: 1) a semantic model of a production system's hierarchical structure, 2) a formalized process description and 3) an OPC UA information model. As a proof of concept we demonstrate our approach using a sample server based on OPC UA for Machinery Companion Specifications.

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“…Such enabling technologies, often referred to as Industry 4.0 [3], have facilitated the implementations of intelligent design [4,5], management [6,7], data analytics [8] and control approaches [9,10]. The Digital Twin (DT) plays a key role in this context [11] because it is the virtual (digital) counterpart of a real system, equipped with a set of models and algorithms to support decision-making, a continuous flow of data sending from the real manufacturing system and a control bus to implement decisions back to the system [12,13]. The bidirectional and continuous flow of data asks for appropriate communication technologies and algorithms.…”

Section: Introductionmentioning

confidence: 99%

A mathematical foundation to support bidirectional mappings between digital models: an application of multi-scale modelling in manufacturing

Terkaj

Urgo

et al. 2022

Proc. R. Soc. A.

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With manufacturing going through the Industry 4.0 revolution, a vast amount of data and information exchange leads to an increase in complexity of digitized manufacturing systems. To tackle such complexity, one solution is to design and operate a digital twin model under different levels of abstraction, with different levels of detail, according to the available information and scope of the model. To support efficient, coherent and stable information flows between models with different levels of detail, a mathematical structure, called a delta lens, has been explored and developed to support rigorous bidirectional transitions between the models. To support different types of abstractions in manufacturing, a hybrid delta lens has been proposed and its formal representation is developed to support the generalization of its structure and properties. Benefits of the proposed hybrid delta lenses are demonstrated through an application to an industrial case to support the modelling of an automatic, high-throughput assembly line.

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Potential for combining semantics and data analysis in the context of digital twins

Cited by 20 publications

References 61 publications

Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Digital Twin Meets Knowledge Graph for Intelligent Manufacturing Processes

Accessing and Interpreting OPC UA Event Traces based on Semantic Process Descriptions

A mathematical foundation to support bidirectional mappings between digital models: an application of multi-scale modelling in manufacturing

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