The development of miniaturized embedded inductors holds crucial significance in advancing modern electronic devices, contributing to their size reduction, enhanced efficiency, and improved performance. This study introduces a groundbreaking, fully additive manufacturing process designed for fabricating miniaturized embedded double-stacked copper spiral inductors. The Sequential Build Up-Covalent Bonded Metallization (SBU-CBM) method serves as the foundation for this novel approach. The experimental process evolves in three stages. In Stage I, the first layer of polyurethane (PU1) is initially spin-coated onto the FR-4 base substrate. The lower embedded copper spiral inductor is then fabricated on top of PU1, employing the SBU-CBM method. Moving to Stage II, the second layer of polyurethane (PU2) is spin-coated onto the existing PU1 layer. Subsequently, a single microvia is created and copper-plated using the SBU-CBM method, establishing a crucial vertical connection between the upper and lower embedded copper spiral inductors. Finally, Stage III involves the fabrication of the upper embedded copper spiral inductor on PU2, utilizing the SBU-CBM method. Optical microscopy and X-ray Computed Tomography (XCT) images confirm the successful formation of embedded double-stacked copper spiral inductors, a configuration where two embedded copper spiral inductors are interconnected through a copper microvia. Notably, the copper strip lines within the spiral inductor configuration are miniaturized to a width of 10 μm, while the diameter of the microvias is reduced to 10 μm, indicating the miniaturization precision achieved through this novel additive manufacturing process.

Interoperability is a central problem in digitization and sos engineering, which concerns the capacity of systems to exchange information and cooperate. The task to dynamically establish interoperability between heterogeneous cps at run-time is a challenging problem. Different aspects of the interoperability problem have been studied in fields such as sos, neural translation, and agent-based systems, but there are no unifying solutions beyond domain-specific standardization efforts. The problem is complicated by the uncertain and variable relations between physical processes and human-centric symbols, which result from, e.g., latent physical degrees of freedom, maintenance, re-configurations, and software updates. Therefore, we surveyed the literature for concepts and methods needed to automatically establish sos with purposeful cps communication, focusing on machine learning and connecting approaches that are not integrated in the present literature. Here, we summarize recent developments relevant to the dynamic interoperability problem, such as representation learning for ontology alignment and inference on heterogeneous linked data; neural networks for transcoding of text and code; concept learning-based reasoning; and emergent communication. We find that there has been a recent interest in deep learning approaches to establishing communication under different assumptions about the environment, language, and nature of the communicating entities. Furthermore, we present examples of architectures and discuss open problems associated with ai-enabled solutions in relation to sos interoperability requirements. Although these developments open new avenues for research, there are still no examples that bridge the concepts necessary to establish dynamic interoperability in complex sos, and realistic testbeds are needed.

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 success of the ongoing fourth industrial revolution largely depends on our ways to cope with the novel design challenges arising from a combination of an enormous increase in process and product complexity, as well as the expected autonomy and self-organization of complex and diverse industrial hardware-software installments, often called systems-of-systems. In this paper, we employ the service-oriented architectural paradigm, as materialized in the Eclipse Arrowhead framework, to represent modern systems engineering principles and their open structural principles and, thus, relevance to flexible and adaptive systems. As for adequately capturing the structural aspect, we propose using model-based engineering techniques and, in particular, a SysML-based specialization of systems modeling. The approach is illustrated by a real-life use-case in industrial automation.

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.

The semiconductor industry demands high input/output (I/O) density, requiring sub-l0-micrometer microvia. Here we propose a novel, fully additive, economical approach for creating and copper plating of microvias. The experimental process consisted of three stages. In Stage I, a polyurethane layer was spin-coated onto a FR-4 PCB base, followed by target copper layer deposition using the sequential build-up-covalent bonded metallization (SBU -CBM) method. In Stage II, first another layer of polyurethane was spin-coated on the top of the target copper layer, and then a microvia was created on the polyurethane layer using a picosecond pulsed ultraviolet (UV) laser. Finally, in Stage III, the SBU-CBM method was used to selectively copper plating of the microvia. Optical microscopy and cross-section scanning electron microscopy (SEM) images confirmed the successful formation and copper plating of sub-l0 micrometer microvia.

In the modern manufacturing industry, collaborative architectures are growing in popularity. We propose an Industry 5.0 value-driven manufacturing process automation ecosystem in which each edge automation system is based on a local cloud and has a service-oriented architecture. Additionally, we integrate cloud-based collaborative learning (CCL) across building energy management, logistic robot management, production line management, and human worker Aide local clouds to facilitate shared learning and collaborate in generating manufacturing workflows. Consequently, the workflow management system generates the most effective and Industry 5.0-driven workflow recipes. In addition to managing energy for a sustainable climate and executing a cost-effective, optimized, and resilient manufacturing process, this work ensures the well-being of human workers. This work has significant implications for future work, as the ecosystem can be deployed and tested for any industrial use case.

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.