D. Braun, F. Biesinger, N. Jazdi, und M. Weyrich, „A concept for the automated layout generation of an existing production line within the Digital Twin“, 8th CIRP Conference on Assembly Technology and Systems, 29 September-1 October 2020, Athens, 2021.
Zusammenfassung
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.BibTeX
N. Sahlab, D. Braun, T. Jung, N. Jazdi, und M. Weyrich, „A Tier-based Model for Realizing Context-Awareness of Digital Twins“, in 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 07-10 Sept. 2021, Västerås, Sweden, 2021.
Zusammenfassung
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.BibTeX
D. Braun, W. Schloegl, und M. Weyrich, „Automated data-driven creation of the Digital Twin of a brownfield plant“, in 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), 07-10 Sept. 2021, Västerås, Sweden, 2021.
Zusammenfassung
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.BibTeX
D. Braun, W. Schlögl, und M. Weyrich, „Eine Methodik zur Erstellung multi-dimensionaler Modelle des Digitalen Zwillings für automatisierte Produktionssysteme“, Automation 2021, VDI Verlag GmbH Düsseldorf, pp. 29-42, 29. und 30 June 2021, 2021.
Zusammenfassung
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.BibTeX
D. Braun, B. Ashtari, und M. Weyrich, Integration of data and software into the Digital Twin via AML. Rainer Drath, 2021.
Zusammenfassung
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.BibTeX
B. Maschler, D. Braun, N. Jazdi, und M. Weyrich, „Transfer Learning as an Enabler of the Intelligent Digital Twin“, in 31st CIRP Design Conference, Enschede, The Netherlands, May 2021, 2021.
Zusammenfassung
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.BibTeX