Now showing 1 - 10 of 50
  • Publication
    Metadata only
    On the Challenges of Transforming UVL to IVML
    (2024)
    Agarwal, Prankur 
    ;
    Feichtinger, Kevin 
    ;
    ; ;
    Rabiser, Rick 
    Software product line techniques encourage the reuse and adaptation of software components for creating customized products or software systems. These different product variants have commonalities and differences, which are managed by variability modeling. Over the past three decades, both academia and industry have developed numerous variability modeling methods, each with its own advantages and disadvantages. Many of these methods have demonstrated their utility within specific domains or applications. However, comprehending the capabilities and differences among these approaches to pinpoint the most suitable one for a particular use case remains challenging. Thus, new modeling techniques and tailored tools for handling variability are frequently created. Transitioning between variability models through transformations from different approaches can help in understanding the benefits and drawbacks of different modeling approaches. However, implementing such transformations presents challenges, such as semantic preservation and avoiding information loss. TRAVART is a tool that helps with transitioning between different approaches by enabling the transformation of variability models into other variability models of different types. This paper discusses the challenges for such transformations between UVL and IVML. It also presents a one-way transformation from the UVL to IVML with as little information loss as possible.
  • Publication
    Metadata only
    Performance Evaluation of BaSyx based Asset Administration Shells for Industry 4.0 Applications
    The Asset Administration Shell (AAS) is an upcoming information model standard, which aims at interoperable modeling of “assets”, i.e., products, machines, services or digital twins in IIoT/Industry 4.0. Currently, a number of IIoT-platforms use proprietary information models similar to AAS, but not a common standard, which affects interoperability.A key question for a broad uptake is if AAS can be applied in a performant and scalable manner. In this paper, we examine this question for the open source Eclipse BaSyx middleware. To explore capabilities and possible performance limitations, we present four experiments measuring the performance of experimental AAS in BaSyx and, within the context set by our experiments, i.e., 10-1000 AAS instances, can conclude good scalability.
      11
  • Publication
    Metadata only
    Software in Cyberphysischen Produktionssystemen
    (2023)
    Feichtinger, Kevin 
    ;
    Meixner, Kristof 
    ;
    Rinker, Felix 
    ;
    Koren, Istvan 
    ;
    ;
    Heinemann, Tonja 
    ;
    Holtmann, Jorg 
    ;
    Konersmann, Marco 
    ;
    Michael, Judith 
    ;
    Neumann, Eva-Maria 
    ;
    Pfeiffer, Jerome 
    ;
    Rabiser, Rick 
    ;
    Riebisch, Matthias 
    ;
    Um den effektiven und effizienten Betrieb von Cyberphysischen Produktionssystemen (CPPSen) sicherzustellen, spielt Software eine zunehmend wichtige Rolle. Die enormen Fortschritte bei Softwareentwicklungsmethoden, welche in den letzten Jahren erzielt wurden, scheinen jedoch die aktuellen Herausforderungen der Industrie nicht zu erfüllen, weil diese die Industrie nicht oder nur langsam erreichen. In diesem Beitrag werden die Herausforderungen für die Softwareentwicklung in CPPSen aus Sicht von neun Industrievertretern aus acht europäischen Unternehmen unterschiedlicher Größe diskutiert. Um den digitalen Transformationsprozess für eine zukunftsfähige Produktion zu begleiten, wurden aus den beschriebenen Herausforderungen Perspektiven für die Forschung erarbeitet. Die Umsetzung dieser Ziele ist vor dem Hintergrund von ökonomischen, sozialen und Nachhaltigkeitsanforderungen notwendig.
  • Publication
    Metadata only
    Developing an AI-Enabled IIoT Platform - Lessons Learned from Early Use Case Validation
    (Springer International Publishing, 2023) ;
    Palmer, Gregory 
    ;
    Reimer, Svenja 
    ;
    Trong Vu, Tat 
    ;
    Do, Hieu 
    ;
    Laridi, Sofiane 
    ;
    ;
    Niederée, Claudia 
    ;
    Hildebrandt, Thomas 
    ;
    Batista, Thais
    ;
    Bures, Thomas
    ;
    Raibulet, Claudia
    ;
    Muccini, Henry
    For a broader adoption of AI in industrial production, adequate infrastructure capabilities and ecosystems are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration. IIoT platforms can play a major role here by providing a unified layer for the heterogeneous Industry 4.0/IIoT context. However, existing IIoT platforms still lack required capabilities to flexibly integrate reusable AI services and relevant standards such as Asset Administration Shells or OPC UA in an open, ecosystem-based manner. This is exactly what our next level Intelligent Industrial Production Ecosphere (IIP-Ecosphere) platform addresses, employing a highly configurable low-code based approach. In this paper, we introduce the design of this platform and discuss an early evaluation in terms of a demonstrator for AI-enabled visual quality inspection. This is complemented by insights and lessons learned during this early evaluation activity.
  • Publication
    Metadata only
    Asset Administration Shells, Configuration, Code Generation: A power trio for Industry 4.0 Platforms
    (IEEE, 2023) ;
    Niederée, Claudia 
    The development of intelligent solutions for manufacturing is a challenging task. Industry 4.0 platforms can provide a unifying layer here. However, flexible AI support, openness for evolving service and components from different vendors and adaptability to the diverse and changing requirements is required from such a platform to boost IIoT development. For this purpose, our approach combines - as a "power trio" - (1) wide use of Asset Administration Shells (AAS) for targeting device, component and service heterogeneity, with (2) configuration support for dealing with the diverse and changing requirements and (3) code generation for cost-effective creation of customer specific platform instances, AAS and AI-based Industry 4.0 applications on top of the IIP-Ecosphere platform. The platform has been implemented based on vertically scaled AAS and evaluated with two Industry 4.0 demonstrators. In this context, we discuss the experiences we made with our approach.
      1
  • Publication
    Metadata only
    Developing an AI-enabled Industry 4.0 platform - Performance experiences on deploying AI onto an industrial edge device
    (Gesellschaft für Informatik e.V., 2023) ;
    Palmer, Gregory 
    ;
    Niederée, Claudia 
    Maximizing the benefits of AI for Industry 4.0 is about more than just developing effective new AI methods. Of equal importance is the successful integration of AI into production environments. One open challenge is the dynamic deployment of AI on industrial edge devices within close proximity to manufacturing machines. Our IIP-Ecosphere platform was designed to overcome limitations of existing Industry 4.0 platforms. It supports flexible AI deployment through employing a highly configurable low-code based approach, where code for tailored platform components and applications is generated.In this paper, we measure the performance of our platform on an industrial demonstrator and discuss the impact of deploying AI from a central server to the edge. As result, AI inference automatically deployed on an industrial edge is possible, but in our case three times slower than on a desktop computer, requiring still more optimizations.
      4
  • Publication
    Metadata only
    Analyzing and Improving the Performance of Continuous Container Creation and Deployment
    (Gesellschaft für Informatik e.V., 2023) ; ;
    Gesellschaft für Informatik e.V.
    ;
    Gesellschaft für Informatik e.V.
    Continuous Deployment automates the delivery of new versions of software systems. To ease installation and delivery, often container virtualization is applied. In this paper, we discuss the impact of different (Docker) container image creation techniques for variant-rich Industry 4.0 applications. Our results show that a combination of techniques like container image stacking or semantic fingerprinting can save up to 59% build time and up to 89% deployment time, while not affecting the container startup time.
      10
  • Publication
    Metadata only
    Experiences in Collecting Requirements for an AI-enabled Industry 4.0 Platform
    (Gesellschaft für Informatik e.V., 2023) ; ;
    Gesellschaft für Informatik e.V.
    Industry 4.0 software platforms target creation, provisioning and operation of industrial applications, e.g., on a shopfloor. Recent advances in Artificial Intelligence (AI), one pillar of Industry 4.0, lead to new demands. The funded project IIP-Ecosphere designs a novel, AI-enabled Industry 4.0 platform. As a basis, we applied two complementing requirements views, namely usage and functional view inspired by IIRA, and collected 67 usage view scenarios and 141 top level functional requirements. In this paper, we summarize our experiences on the requirements collection and discuss their effect on the yet realized platform.
      6
  • Publication
    Metadata only
    Performance comparison of TwinCat ADS for Python and Java
    (Gesellschaft für Informatik e.V., 2023) ; ;
    Schreiber, Per 
    ;
    Wienrich, Svenja 
    ;
    Gesellschaft für Informatik e.V.
    Real-time and in-process measurements are important in the manufacturing domain, e.g., for real-time process monitoring. For performance reasons, such data is often processed in virtualized environments on edge devices, as e.g., provided by the company Beckhoff. For exploring modern AI methods, integration with high-level languages such as Python or even with Industry 4.0 platforms for advanced data flows is needed. In this paper, we analyze the read/write perfor mance of a Beckhoff device integrated via Python or Java. For our experiments, we use a simulation on a PC as well as a networked setup with a Beckhoff device. We show that the Java-based solution is faster than the Python one by 2-3 times. We also show that small arrays can be read as fast as a single value, that there is no difference between operations for small or big data types and that there is no difference between reading and writing data.
      10
  • Publication
    Metadata only
    Requirements for an AI-enabled Industry 4.0 Platform – Integrating Industrial and Scientific Views
    (ThinkMind, 2022-04) ;
    Stichweh, Heiko 
    ;
    Intelligent manufacturing is one goal of smart industry/ Industry 4.0 that could be achieved through Artificial Intelligence (AI). Flexibly combining AI methods and platform capabilities, such as dynamic offloading of code close to production machines, security or interoperability mechanisms are major demands in this context. However, recent Industry 4.0 software platforms fall short in various of these demands, in particular in upcoming ecosystem scenarios, e.g., when data or services shall be shared across platforms or companies without vendor lock-ins. The aim of the funded Intelligent Industrial Production (IIP) IIP-Ecosphere project is to research concepts and solutions for ‘easy-to-use’ AI in Industry 4.0 and to demonstrate the results in a prototypical software platform. Core questions are which demands shall drive the development of such a platform and how a feasible set of requirements can be determined that balances scientific and industrial interests. In this paper, we discuss our approach on eliciting requirements in this context for two interlinked requirements perspectives, a usage and a functional view. In summary, we collected 67 usage view activities / scenarios and 141 top-level requirements with 179 detailing sub-requirements. About 35% of the requirements have so far been realized in a prototype and some of the identified concepts are currently being taken up by a standardization initiative for edge devices in Industry 4.0.
      11