Simulations offer opportunities in the examination of manufacturing processes. They represent various aspects of the production process and the associated production systems. However, often a single simulation does not suffice to provide a comprehensive understanding of specific process settings. Instead, a combination of different simulations is necessary when the outputs of one simulation serve as the input parameters for another, resulting in a sequence of simulations. Manual planning of simulation sequences is a demanding task that requires careful evaluation of factors like time, cost, and result quality to choose the best simulation scenario for a given inquiry. In this paper, an information model is introduced, which represents simulations, their capabilities to generate certain knowledge, and their respective quality criteria. The information model is designed to provide the foundation for automatically generating simulation sequences. The model is implemented as an extendable and adaptable ontology. It utilizes Ontology Design Patterns based on established industrial standards to enhance interoperability and reusability. To demonstrate the practicality of this information model, an application example is provided. This example serves to illustrate the model's capacity in a real-world context, thereby validating its utility and potential for future applications.

In future cyber-physical systems, such as smart factories and energy grids, ontologies can serve as the enabler for semantically precise communication as well as for knowledge representation and reasoning. Multi-agent systems have shown to be a suitable software development paradigm for cyber-physical systems and may well profit from harnessing ontologies in terms of reduced engineering effort and better interoperability. This contribution presents a development methodology for ontologies that enable communication and reasoning in Multi-Agent Systems for cyber-physical systems. The methodology is unique in addressing a set of requirements specific to this application domain.

Ontologies provide an effective way for describing and using knowledge of a specific domain. In engineering workflows the reusability and quick adoption of knowledge is needed for solving several tasks in efficient ways. Engineering data is mostly structured in hierarchical documents and exchange formats and not represented in ontologies. Therefore a connection between engineering data and the knowledge in ontologies is needed. In this article we present a bridge concept for connecting engineering data with an OWL-based ontology. For this we use an example ontology containing security knowledge of automation systems.

To facilitate the automated evaluation of energy-efficiency aspects of a system in the early lifecycle phase of engineering, a consistent semantic definition of the relevant terminology as well as the interrelations between those terms is required. For this purpose, a lightweight ontology named OntoENERGY has been developed, which allows for continuous handling of energy-efficiency issues in technical systems throughout their entire lifecycle. To verify OntoENERGY, a simulation model based on a real test bed of an automated plant process was modeled, analyzed, and assessed, with a focus on energy consumption and the related information. This allows optimization potential to be identified and enables a direct assessment, with the aid of a simulation model.