Dominik Braun

Abstract

The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.

Abstract

The huge potential of the Digital Twin and its benefits in use cases such as virtual commissioning or reconfiguration planning for production systems is often described and well known. One reason that the usage of Digital Twins is becoming increasingly important are current trends, such as individualized mass production, which lead to more frequent reconfigurations of production systems or require higher flexibility. At the same time, many production systems have been in productive use for some time, so-called brownfield systems. Most of them do not have a Digital Twin, but would benefit from one, if available. However, the creation of a Digital Twin is a significant additional effort, due to the creation of the underlying models and the relations between them. Some promising approaches for the creation of domain-specific models already exist, which can be used for the creation of the unconnected parts of a Digital Twin. However, there are still few results for the creation of inter-domain relations, which cause problems in the creation of Digital Twins. This paper therefore presents a methodology for the automated reconstruction of functional relations as well as the separation of functionally related component groups of the plant and its software. For this purpose, the methodology imports the native PLC code via XML, extracts the relevant information from the code and enriches the resulting information model with explicit information about implicit relations in the code. Based on this, various elements of the PLC code are analyzed and the relationship of individual mechatronic components, that are available as signals, are examined and quantified. Thus, a part of the required relations for a Digital Twin is recognized automatically. The methodology and its benefits for the Digital Twin creation is presented and explained using a modular production system with several manufacturing stations as a proof-of-concept.

Abstract

Digital Twins, which serve as virtual representations, are becoming increasingly important to meet the needs of future industrial automation. Digital Twins are mostly created for specific use cases, facing various requirements e.g. reliable intellectual property protection, interoperability, flexibility, or extendability during implementation. For this purpose, decisions regarding structure and technology have to be made before implementation. In this paper, we discuss relevant requirements based on a proposed procedure for the derivation of project-specific intelligent Digital Twin implementations. The influences and correlations of project requirements are discussed concerning the choice of intelligent Digital Twins implementation structure and technology. In addition, the paper outlines their advantages and disadvantages. The proposed procedure is exemplarily illustrated using an intelligent Digital Twin utilized for a modular offshore-platform producing green hydrogen and derivatives.

Abstract

Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.

Abstract

The huge potential of the Digital Twin and its benefits in use cases such as virtual commissioning or reconfiguration planning for production systems is often described and well known. One reason that the usage of Digital Twins is becoming increasingly important are current trends, such as individualized mass production, which lead to more frequent reconfigurations of production systems or require higher flexibility. At the same time, many production systems have been in productive use for some time, so-called brownfield systems. Most of them do not have a Digital Twin, but would benefit from one, if available. However, the creation of a Digital Twin is a significant additional effort, due to the creation of the underlying models and the relations between them. Some promising approaches for the creation of domain-specific models already exist, which can be used for the creation of the unconnected parts of a Digital Twin. However, there are still few results for the creation of inter-domain relations, which cause problems in the creation of Digital Twins. This paper therefore presents a methodology for the automated reconstruction of functional relations as well as the separation of functionally related component groups of the plant and its software. For this purpose, the methodology imports the native PLC code via XML, extracts the relevant information from the code and enriches the resulting information model with explicit information about implicit relations in the code. Based on this, various elements of the PLC code are analyzed and the relationship of individual mechatronic components, that are available as signals, are examined and quantified. Thus, a part of the required relations for a Digital Twin is recognized automatically. The methodology and its benefits for the Digital Twin creation is presented and explained using a modular production system with several manufacturing stations as a proof-of-concept.

Abstract

The use of the Digital Twin as a promising technology during the reconfiguration of automated systems enables to meet the challenges of increasing product diversity and shortening product life cycles in today's industry. The use of Digital Twins supports engineers in all phases of the life cycle of a production line. Over time, however, production facilities are often modified and improved, while at the same time the created models during the engineering process of the system remain unchanged and no longer correspond to the real facility. Manual updating of the positions in the digital layout is very time-consuming and therefore expensive. This paper presents a concept for the automatic update of the layout of a production line. In this concept, the positions of the robots and their active and passive peripheral devices are automatically positioned in a digital plant model using information from the current configuration of the robots in a production line. Therefore, engineers can use the resulting synchronized digital plant model, Digital Twin of the system, to further optimize or expand the production plant.

Abstract

Digital Twins are being increasingly used in manufacturing industry to support the whole plant lifecycle. The constantly changing environmental parameters, interactions between various systems and their Digital Twin as well as changes inside the Digital Twin influences the context of the available information. This context information is insufficiently considered to analyze process and optimize the interaction. This article presents an approach to model the internal and external context of a system using a graph-based context tier model. The usage and benefits.

Abstract

The success of the reconfiguration of existing manufacturing systems, so called brownfield systems, heavily relies on the knowledge about the system. Reconfiguration can be planned, supported and simplified with the Digital Twin of the system providing this knowledge. However, digital models as the basis of a Digital Twin are usually missing for these plants. This article presents a data-driven approach to gain knowledge about a brownfield system to create the digital models of a Digital Twin and their relations. Finally, a proof of concept shows that process data and position data as data sources deliver the relations between the models of the Digital Twin.

Abstract

Der Digitale Zwillinge einer Produktionsanlage kann für viele Use Cases sehr hilfreich sein. Dazu muss die Synchronität des Digitalen Zwillings mit dem realen Produktionssystem sichergestellt werden. Diese herausfordernde Aufgabe kann durch unterschiedliche Methoden unterstützt oder (teil-) automatisiert werden. Ein wichtiger Aspekt, der für Digitale Zwillinge beachtet werden muss, sind die Relationen zwischen den einzelnen Modellen der Dimensionen Mechanik, Elektrik und Software. Daher werden die gängigen Methoden zur Synchronisierung und Erstellung digitaler Modelle aus multiplen Dimensionen vorgestellt und die Relationsbildung betrachtet. Die Ankerpunktmethode als vielversprechendste Methode wird anschließen an einer Beispielsanlage untersucht und deren Grenze aufgezeigt. Basierend auf diesen Ergebnissen wird eine Methodik vorgestellt, die sich mit der Rekonstruktion der digitalen Modelle und deren Relationen beschäftigt. Dabei liegt der Fokus auf bereits existierenden, produktiv eingesetzten Produktionssystemen, sogenannte Brownfield Anlagen, als das häufigste Einsatzfeld.

Abstract

Digital Twins and the Internet-of -Things (IoT) are going to boost many applications based on information and communication technology. A study of COOM19 indicates that the technology of a Digital Twin is about to be established in Industry. It was found that “62 percent (of companies questioned) are either in the process of establishing Digital Twin use or plans to do so”. Many authors agree that these new technologies will connect virtually everything from customers to machines and logistics. AutomationML is an important standard and relevant enabler for the creation, adaption and management of the Digital Twin. In the near future, it is expected that machines will communicate with one another, logistics and machinery will be self-controlled and all plans can be projected by simulation, which supports decision-making. From this perspective, the Digital Twin is a novel and enabling technology, which can be spread easier and faster with the use of AML. The demands of markets to release constantly market-driven innovations, compels industrial manufacturing companies to increase both the use of automated production systems and their reconfiguration during their lifecycle. Increasing product variety and shortening product life cycles require a fast and inexpensive reconfiguration of existing production systems AJS+18a. To face these challenges, one solution is to use the Digital Twins of automated manufacturing systems. The value-add of a Digital Twin for reconfiguration, promises the reconfiguration time to be reduced and thus an increase of the system availability as well as customer-specific products to be manufactured at short notice. A simulative environment using a Digital Twin enables automated systems to be quickly and easily reconfigured by a virtual test in simulation. The recommissioning and reconfiguration of the real manufacturing system therefore requires less working time, is less error prone and thereby, reducing costs. However, such savings are only worthwhile if the effort of creating the digital twin is covered by the savings from the above-mentioned benefits. It is evident that the Digital Twin should be implemented by integrated software systems, such as product lifecycle management (PLM) systems, and should also be based on standards such as AML. The implementation of AML for PLM systems combines the benefits of both and enables the managed utilization of many different tools with standardized interface and thereby helps reducing the barriers for its creation.

Abstract

Digital Twins have been described as beneficial in many areas, such as virtual commissioning, fault prediction or reconfiguration planning. Equipping Digital Twins with artificial intelligence functionalities can greatly expand those beneficial applications or open up altogether new areas of application, among them cross-phase industrial transfer learning. In the context of machine learning, transfer learning represents a set of approaches that enhance learning new tasks based upon previously acquired knowledge. Here, knowledge is transferred from one lifecycle phase to another in order to reduce the amount of data or time needed to train a machine learning algorithm. Looking at common challenges in developing and deploying industrial machinery with deep learning functionalities, embracing this concept would offer several advantages: Using an intelligent Digital Twin, learning algorithms can be designed, configured and tested in the design phase before the physical system exists and real data can be collected. Once real data becomes available, the algorithms must merely be fine-tuned, significantly speeding up commissioning and reducing the probability of costly modifications. Furthermore, using the Digital Twin’s simulation capabilities virtually injecting rare faults in order to train an algorithm’s response or using reinforcement learning, e.g. to teach a robot, become practically feasible. This article presents several cross-phase industrial transfer learning use cases utilizing intelligent Digital Twins. A real cyber physical production system consisting of an automated welding machine and an automated guided vehicle equipped with a robot arm is used to illustrate the respective benefits.

Abstract

Increasing product variety and shortening product lifecycles require a fast and inexpensive reconfiguration of existing manufacturing systems. A synchronized Digital Twin of the manufacturing system is one solution to face these challenges. In order to use the Digital Twin for reconfiguration, a major challenge is to keep the developed Digital Twin of a manufacturing system, which was created during the engineering process, synchronized with the real system after commissioning. To automatically synchronize the cross-domain models of a Digital Twin after the commissioning of a manufacturing system, the authors introduced the Anchor-Point-Method in their previous papers. In this paper, the realization of the Anchor-Point-Method based on an assistance system is presented and the functionality is evaluated. This assistance system enables having an up-to-date Digital Twin of a manufacturing system available during the entire life-cycle of a system. Finally, a qualitative and quantitative evaluation of the advantages of a synchronized Digital Twin for the reconfiguration of a manufacturing system is presented. For this purpose, an automated system has been digitally and physically designed and built. On this system, a reconfiguration using the synchronized Digital Twin was performed and compared with another reconfiguration without Digital Twin collected through a survey. The results show that the Digital Twin can reduce the time of the reconfiguration process by up to 58 percent.