Sustainability considerations and Environmental Product Declarations (EPD) are increasingly imposed by legal frameworks to reduce emissions and ensure compliance. Initiatives such as the Digital Product Passport aim at uniformly and digitally representing data for transparency and facilitated use throughout the product’s lifecycle. A challenge remains in the heterogeneity of data and formats, as well as in the assessment and comprehension of the environmental indicators by non-domain experts. Focusing on the early design phase, a potential lies in relating product design to potential environmental impact within a semantic representation.In this contribution, we explore the use of the Asset Administration Shell (AAS) during the early design phase to incorporate sustainability criteria and enhance their comprehensibility. We focus on variant generation and management inspired by software product line and by representing the AAS, along with additional domain knowledge, in the form of a knowledge graph. We present an EPD submodel as part of the AAS. Its RDF-based representation allows for property querying for direct comparison across various AAS submodel instances. The concept is demonstrated for the design of a sustainable switchgear.

Advancements in the Internet of Things (IoT) applied by automation systems lead to an increasing and flexible interconnection of various devices and the generation of voluminous data. In the domain of home automation for instance, having a multitude of interconnected data leveraged to knowledge could enhance user-centeredness and energy efficiency, among other use cases. However, the data as well as data sources are dynamic and heterogeneous, posing a great challenge to unfold their full capabilities. Thus, a unifying and correspondingly dynamic architecture is needed for supporting the flexibility and extensibility of such systems and data sources. In this paper, we present the concept and implementation of a context-aware INTElligent LIVing (inteLiv) architecture. Consisting of a data layer, a context layer and a service layer, the architecture abstracts the heterogeneous data of the linked devices via a middleware. The resulting system enables the collection of data across networked heterogeneous devices and models the collected data by using a unified dynamic context model. A hybrid approach was chosen for the designed context model: A combination of an ontology-based and property graph-based model represent a core aspect of this work as opposed to common static ontological representations and pre-defined context-based use cases. In the following contribution, the inteLiv ontology is first defined to describe the concepts of the intelligent living system. Based on the modeled context, reasoning algorithms also enable the derivation of new context information. The context is stored as a knowledge graph and can be used for context-aware applications, which can be designed during runtime. Furthermore, a user interaction with the system is possible via a web interface.

The creation of a Digital Twin for existing manufacturing systems, so-called brownfield systems, is a challenging task due to the needed expert knowledge about the structure of brownfield systems and the effort to realize the digital models. Several approaches and methods have already been proposed that at least partially digitalize the information about a brownfield manufacturing system. A Digital Twin requires linked information from multiple sources. This paper presents a graph-based approach to merge information from heterogeneous sources. Furthermore, the approach provides a way to automatically identify templates using graph structure analysis to facilitate further work with the resulting Digital Twin and its further enhancement.

Current industrial automation systems are facing increasing dynamics. Thus, acquiring and managing heterogeneous data within the Digital Twin to enable decision making is necessary although challenging. Knowledge Graphs unify and relate data, enabling the derivation of new insights. In this contribution, an approach for context-enriched modeling of cyber-physical production systems is proposed, in order to realize a Knowledge Graph enhanced intelligent Digital Twin further considering the context. Therefore, the modeling approach of the Knowledge Graph considers context and serves as a base for the graph embeddings to gain further knowledge about the production system. This knowledge is used within the architecture of the intelligent Digital Twin. The resulting benefits, i.e. diverse manifestations of an improved decision making, are highlighted by the use case of self-organized reconfiguration management.

Automation systems are increasingly being used in dynamic and various operating conditions. With higher flexibility demands, they need to promptly respond to surrounding dynamic changes by adapting their operation. Context information collected during runtime can be useful to enhance the system's adaptability. Context-aware systems represent a design paradigm for modeling and applying context in various applications such as decision-making. In order to address context for automation systems, a state-of-the-art assessment of existing approaches is necessary. Thus, the objective of this work is to provide an overview on the design and applications of context and context models for automation systems. A systematic literature review has been conducted, the results of which are represented as a knowledge graph.

Systematic Literature Reviews aim at investigating current approaches to conclude a research gap or determine a futuristic approach. They represent a significant part of a research activity, from which new concepts stem. However, with the massive availability of publications at a rapid growing rate, especially digitally, it becomes challenging to efficiently screen and assess relevant publications. Another challenge is the continuous assessment of related work over a long period of time and the consequent need for a continuous update, which can be a time-consuming task. Knowledge graphs model entities in a connected manner and enable new insights using different reasoning and analysis methods. The objective of this work is to present an approach to partially automate the conduction of a Systematic Literature Review as well as classify and visualize the results as a knowledge graph. The designed software prototype was used for the conduction of a review on context-awareness in automation systems with considerably accurate results compared to a manual conduction.

Digital Twins are being increasingly used in manufacturing industry to support the whole plant lifecycle. The constantly changing environmental parameters, interactions between various systems and their Digital Twin as well as changes inside the Digital Twin influences the context of the available information. This context information is insufficiently considered to analyze process and optimize the interaction. This article presents an approach to model the internal and external context of a system using a graph-based context tier model. The usage and benefits of the concept are shown for a flexible production system.

Networked and intelligent cyber-physical systems are being increasingly applied in automation technology. Surrounding parameters representing the ambient environment and the users interacting with these systems can greatly influence their operation and tend to be heterogeneous and dynamic. There is insufficient consideration for these parameters and how they relate to the system and can contribute to optimizing its analysis and operation. Enabling context-awareness for cyber-physical systems presents an approach to consider surrounding and situational factors. In this contribution, the design of context-aware cyber-physical systems is addressed with the goal to first introduce a fitting definition for context-aware cyber-physical systems as well as propose an approach to dynamically model the system's context in relation to its user and environment. The concept is shown for a user-centric pill dispenser, based on which the role as well as applicability of the context model is presented.