Due to the growing environmental and geopolitical challenges nowadays, which are causing supply chain complications, industry and society are facing significant new objections. As a complement and extension to the technology-driven premises of Industry 4.0, the value-driven Industry 5.0 focuses on society and the environment. Human centricity, sustainability, and resilience should become a more integral part of both industrial and societal revolutions. One of the enabler technologies for both is the Digital Twin (DT). In order to make DTs intelligent, they must become active, online, goal-seeking, and anticipatory. To meet these requirements, the characteristics of Multi-Agent Systems (MASs) can be employed. This paper contributes to the bilateral emergence of the two industrial paradigms and establishes an approach for the provision of Intelligent Digital Twins (IDTs) within the Internet of Digital Twins (IoDT). Initially, a DT reference model aligned with already established Industry 4.0 reference models enriched with the goals of Industry 5.0 is developed, followed by an outline of how IDTs can be realized with the characteristics of MAS. The work is substantiated by an architectural design for IDTs choreographing marketplace-oriented production processes with a subsequent prototypical implementation, followed by a proof of concept.

One of the main topics within research activities is the management of research data. Large amounts of data acquired by heterogeneous scientific devices, sensor systems, measuring equipment, and experimental setups have to be processed and ideally be managed by Findable, Accessible, Interoperable, and Reusable (FAIR) data management approaches in order to preserve their intrinsic value to researchers throughout the entire data lifecycle. The symbiosis of heterogeneous measuring devices, FAIR principles, and digital twin technologies is considered to be ideally suited to realize the foundation of reliable, sustainable, and open research data management. This paper contributes a novel architectural approach for gathering and managing research data aligned with the FAIR principles. A reference implementation as well as a subsequent proof of concept is given, leveraging the utilization of digital twins to overcome common data management issues at equipment-intense research institutes. To facilitate implementation, a top-level knowledge graph has been developed to convey metadata from research devices along with the produced data. In addition, a reactive digital twin implementation of a specific measurement device was devised to facilitate reconfigurability and minimized design effort.

In order to empower future production to be ready for Industry 5.0, it needs to be geared towards customer-oriented mass customisation. Yet the necessary connection between production process and product within the control software is not adequately addressed. In the engineering process, especially while control logic design, the digital twins of production machines are not sufficiently considered. The products also need to be suitably specified in terms of their production process. Only then autonomous negotiation algorithms can be established between digital twins of production machines and products. The authors elaborate requirement specifications for digital twins and, based on these, derive specifications for control logic and the knowledge graph of production assets. Finally, the feasibility is substantiated by a demonstrative proof of concept.

The growing complexity and scale of Cyber–Physical Systems (CPSs) have led to an increasing need for the holistic orchestration of multiple Digital Twins (DTs). Therefore, an extension to the UniTwin framework is introduced within this paper. UniTwin is a containerized, cloud-native DT framework. This extension enables the hierarchical aggregation of DTs across various abstraction levels. Traditional DT frameworks often lack mechanisms for dynamic composition at the level of entire systems. This is essential for modeling distributed systems in heterogeneous environments. UniTwin addresses this gap by grouping DTs into composite entities with an aggregation mechanism. The aggregation mechanism is demonstrated in a smart manufacturing case study, which covers the orchestration of a production line for personalized shopping cart chips. It uses modular DTs provided for each device within the production line. A System-Aggregated Digital Twin (S-ADT) is used to orchestrate the individual DTs, mapping the devices in the production line. Therefore, the production line adapts and reconfigures according to user-defined parameters. This validates the flexibility and practicality of the aggregation mechanism. This work contributes an aggregation mechanism for the UniTwin framework, paving the way for adaptable DTs for complex CPSs in domains like smart manufacturing, logistics, and infrastructure.

Semantic technologies and Multi-Agent Systems (MAS) are promising key technologies for making Digital Twins (DTs) intelligent. Due to siloed architectures and the lack of interoperability, many current implementations remain isolated and are limited in terms of intelligence and proactivity. Semantic enrichment provides a common basis of understanding that fosters interoperability and contextual awareness across system boundaries, while MAS approaches enable decentralized, autonomous decision-making processes. Against this background, a systematic literature review of the current research landscape from 2019 to June 2025 examines the state of research on semantic and agent-based enablers for intelligent DTs, bringing together fragmented insights and identifying remaining research gaps and their potential. The findings show that the manufacturing industry dominates this field, using standardized data sources and agent-based orchestration for dynamic scheduling and adaptation, while other sectors are exploring customized ontologies and simpler agent schemes. However, many of these implementations are still in their early stages, lacking closed-loop control or fully autonomous decision-making. The conclusions point to the future need for common semantic standards, unified evaluation strategies, and integration with other artificial intelligence techniques to drive autonomy and proactivity of DT systems.