IIP-Ecosphere – Next Level Ecosphere for Intelligent Industrial Production

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 performance 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.

Continuous Deployment automates the delivery of new versions of software systems. To ease installation and delivery, often container virtualization is applied. In some applications, container images may be subject to variants, as, e.g., device-specific software is needed on Edge devices in Industry 4.0. Here, model-driven approaches can prevent human errors and save development efforts. However, employing a naive approach, creating one container image per variant can be time-consuming. 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.

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.

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.

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.

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.

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.

Kubernetes (K8s) is one of the most frequently used container orchestration tools offering, as it offers a rich set of functions to manage containerized applications, it is customizable and extensible. Container virtualization of applications and their orchestration on heterogeneous resources including edge devices is a recent trend in Industrial Internet of Things (IIoT)/Industry 4.0, where K8s is also applied. However, IIoT/Industry 4.0 is a domain with high standardization requirements. Besides equipment standards, e.g., for electrical control cabinets, there are also demands to standardize network protocols, data formats or information models. Such standards can foster interoperability and reduce complexity or deployment/integration costs. Here, the proprietary communication protocol of K8s and similar orchestrators can be an obstacle for adoption.
To explore this situation from an interoperability and integration perspective, we present in this paper an approach to replace the communication protocol of K8s without modifying its code base. We show by an experiment that applying our approach with three current forms of IIoT communication, namely Message Queuing Telemetry Transport (MQTT), Advanced Message Queuing Protocol (AMQP), and Asset Administration Shell (AAS), does not significantly affect the validity and the performance of K8s.

For a broader adoption of AI in industrial production, adequate infrastructure capabilities are crucial. This includes easing the integration of AI with industrial devices, support for distributed deployment, monitoring, and consistent system configuration.

Für die erfolgreiche Digitalisierung in der Produktion ist die IT-Infrastruktur, zum Beispiel zur einfachen Anbindung von Geräten und Steuerung von Datenflüssen, von zentraler Bedeutung.
Bisherige Lösungen basieren jedoch meist auf proprietären Protokollen und bieten wenig Konfigurations- und Kontrollmöglichkeiten. Basierend auf industriellen Standards, wie z. B. Verwaltungsschalen, wird im Projekt IIP-Ecosphere daher eine offene Code-Basis für die ganzheitliche Umsetzung von Digitalisierungsprojekten in der Produktion entwickelt.