Insufficient resources and high costs are hindering industrial development, potentially impeding adaptation to market demands. Overcoming this challenge necessitates advancements in software engineering techniques to streamline processes and meet industrial requirements. Crucially, automating manual tasks and enhancing interoperability between engineering stages can yield efficiency gains. This paper presents a model-based system engineering approach aimed at automating the transition from design to implementation, incorporating autonomous generation and validation features. Implemented as plugins and utilizing model transformation techniques, this solution targets reducing engineering time and facilitating the adoption of new technologies. Developed, implemented, and tested within the Arrowhead framework, the approach is followed by a discussion on its benefits and limitations.

The rapid transformation of the manufacturing industry under Industry 4.0 demands systems that can quickly adapt to dynamic market conditions and customer needs. Agile manufacturing emphasizes flexibility, adaptability, and real-time responsiveness, posing challenges in run-time value chain analysis (VCA), including cost flows and production times. This paper presents a novel two-stage VCA approach using an activity-based costing mechanism via microservices to address these challenges. The VCA system enables real-time cost accounting and decision-making, supporting both pre- and post-production VCA, contrasting with traditional methods that rely on historical data. The first stage involves top-down cost calculations from resources to microservices, while the second focuses on constructing efficient manufacturing activities based on product requirements, allowing for a granular analysis of costs and production times across microservices, activities, broader business processes, and finally, cost objects (e.g., customized products, batches of products, or customer invoices). The approach is validated through a proof-of-concept implementation of the VCA system integrated with the Eclipse Arrowhead framework and simulating Fischertechnik indexed line milling, drilling, and conveying operations. The results demonstrate the effectiveness of the proposed method in providing detailed insights into costs and production times, enhancing the efficiency and competitiveness of agile manufacturers.

Industry 4.0 has revolutionized industrial automation, with models like RAMI 4.0 providing a structured framework for optimizing value chains and processes. However, the complexity and abstract nature of RAMI 4.0 have limited its practical application, especially due to the lack of clear visualization methods to understand industrial ecosystems. Effective visualization is essential to translate this framework into actionable insights, enabling stakeholders to grasp system interactions, dependencies, and value-creation processes. This paper proposes a multidimensional visualization approach, illustrated through a smart heat pump example, to map information and operational technologies, their interactions, and value chains. Combining 3D visualizations for integrated system overviews with 2D visualizations for task-specific analysis, the approach provides a comprehensive understanding of RAMI 4.0 value chains, enabling stakeholders to address their analytical needs with clarity. It facilitates run-time value chain analysis, offering real-time insights for decision-making during operations. The approach maps industrial systems across RAMI 4.0 axes and aligns them with engineering processes and lifecycle phases, enabling the exploration of system interactions, dependencies, and stakeholder contributions. This supports the analysis of engineering and business processes, optimizes infrastructure, and facilitates smooth technological transitions. It enhances RAMI 4.0’s utility for real-time decision-making and operational efficiency, boosting competitiveness in industrial ecosystems.

A digital twin (DT), the digital counterpart of a physical entity, process, or system, is a pivotal innovation driving the manufacturing industry's digital transformation. DT plays a significant role in product lifecycle management (PLM) and product condition monitoring. However, the diversity of systems and processes involved poses challenges in DT and data management within PLM, particularly regarding efficiency, standardized data mapping, and latency.The paper presents a solution architecture to address these challenges and contribute towards an efficient and cost-effective product lifecycle management system. The architecture focuses on DT's data management and communication aspects, utilizing the edge-based, decentralized Eclipse Arrowhead Framework and EDMtruePLM (Enterprise Data Management True Product Lifecycle Management) for standardized data management and condition monitoring of products.Integrating the ISO 10303 STEP standard for data modeling and the Open Platform Communications Unified Architecture (OPC UA) standard for communication is emphasized, improving the contextual significance of the data and the system's interoperability. A use case implementation is presented, where a fischertechnik assembly line is monitored, capturing sensor data through the PLC's OPC UA server. The sensor data is then aligned with the STEP standard and stored in the EDMTruePLM database for monitoring. 

The fourth and fifth industrial revolutions (Industry 4.0 and Industry 5.0) have driven significant advances in digitalization and integration of advanced technologies, emphasizing the need for sustainable solutions. Smart Energy Systems (SESs) have emerged as crucial tools for addressing climate change, integrating smart grids and smart homes/buildings to improve energy infrastructure. To achieve a robust and sustainable SES, stakeholders must collaborate efficiently through an energy management framework based on the Internet of Things (IoT). Demand Response (DR) is key to balancing energy demands and costs. This research proposes an edge-based automation cloud solution, utilizing Eclipse Arrowhead local clouds, which are based on Service-Oriented Architecture that promotes the integration of stakeholders. This novel solution guarantees secure, low-latency communication among various smart home and industrial IoT technologies. The study also introduces a theoretical framework that employs AI at the edge to create environment profiles for smart buildings, optimizing DR and ensuring human comfort. By focusing on room-level optimization, the research aims to improve the overall efficiency of SESs and foster sustainable energy practices.

Increasing complexity and data-generation rates in cyber-physical systems and the industrial Internet of things are calling for a corresponding increase in AI capabilities at the resource-constrained edges of the Internet. Meanwhile, the resource requirements of digital computing and deep learning are growing exponentially, in an unsustainable manner. One possible way to bridge this gap is the adoption of resource-efficient brain-inspired “neuromorphic” processing and sensing devices, which use event-driven, asynchronous, dynamic neurosynaptic elements with colocated memory for distributed processing and machine learning. However, since neuromorphic systems are fundamentally different from conventional von Neumann computers and clock-driven sensor systems, several challenges are posed to large-scale adoption and integration of neuromorphic devices into the existing distributed digital–computational infrastructure. Here, we describe the current landscape of neuromorphic computing, focusing on characteristics that pose integration challenges. Based on this analysis, we propose a microservice-based conceptual framework for neuromorphic systems integration, consisting of a neuromorphic-system proxy, which would provide virtualization and communication capabilities required in distributed systems of systems, in combination with a declarative programming approach offering engineering-process abstraction. We also present concepts that could serve as a basis for the realization of this framework, and identify directions for further research required to enable large-scale system integration of neuromorphic devices.

The new Industry 4.0 approach contributes to addressing evolving industrial requirements, which are continuously fueled by changing market demands. This situation leads to growing complexity and considerable increases in development and maintenance costs. A significant portion of engineering time is dedicated to the integration and interconnection of heterogeneous components. The solution for interoperability issues and the reduction in the associated engineering time are thus key tasks for increasing productivity and efficiency. Therefore, this paper provides an engineering approach to create interoperability among heterogeneous systems in Service Oriented Architecture (SOA) based environments by means of generating an autonomous consumer interface code at runtime.

This paper aims to present a novel interoperability solution. The proposed approach makes use of service interface descriptions to dynamically instantiate a new autonomously generated interface that solves service mismatches between a provider and a consumer. This paper includes the definition of the consumer interface generator system, as well as the benefits and challenges associated with the autonomous generation and deployment of a consumer interface code at runtime. To illustrate the potential of this approach, a prototype of the system, which shows positive results, is implemented and tested.

Today’s manufacturing industries use a large suite of protocols and technologies to operate heterogeneous devices and software modules. Some of the most widely used technologies in industrial production are OPC UA (Open Platform CommunicationsUnified Architecture) and ROS (Robot Operating System). Hence, enabling interoperability across these technologies is critical to ensure a smooth production flow. We propose a local cloud-based approach to achieve interoperability between ROSand OPC UA by integrating them with the Eclipse ArrowheadFramework. This integration allows these technologies to operate as independent systems while communicating securely at runtime. In addition to achieving interoperability, this integration supports important industrial aspects such as loose coupling, late binding, and cyber-security, making it a flexible solution.