Virtual commissioning (VC) is an established method for the efficient qualification of machine tool control software. Despite the potential to reduce the time-to-market and increase product quality, the creation of simulation models for VC still remains an elaborate task. The reason for this mainly results from the lack of data interoperability between engineering and simulation tools. To address this problem, we introduce a process for automatic behavior simulation model generation that covers the inter- and intra-company exchange of data in a heterogeneous toolchain. The workflow combines information of supplier components with ECAD engineering data represented through open standards like the Asset Administration Shell (AAS) and the Functional Mockup Interface (FMI). The desired level of integration between different data models is reached through a knowledge graph leveraging the Resource Description Framework (RDF) serialization of the AAS. In addition, a domain ontology for VC is introduced that semantically enriches the AAS data for simulation model generation. The presented concept is successfully validated with engineering data of a machine tool for sheet metal processing. It is shown, that a simulation model can be generated from a circuit diagram with minimum user interaction.

Discrete-event simulation is a widely used method for modeling complex systems and evaluating their performance in production. However, the management of discrete-event simulation models and their associated data can be challenging, especially when dealing with large and heterogeneous data sets. In this paper, we present a knowledge graph-based framework that facilitates the management of discrete-event simulation models by representing them and their associated data as a knowledge graph. Our framework enables the integration of multiple data sources, supports the reuse of existing models, and enables the support for different applications based on the knowledge graph. Specifically, we demonstrate the application of our framework to the management of discrete-event simulation models in a use case of automatic model generation for a production line. Our results show that our framework provides an effective solution for managing discrete-event simulation models and their associated data, enabling users to easily query, update, and extend the models while maintaining their integrity and consistency. Overall, our work contributes to the development of knowledge-based solutions for managing complex systems and supports the broader adoption of discrete-event simulation in practice.

Semantic technologies and knowledge graphs are becoming increasingly important in academia and industry. Nevertheless, they remain a rarity in the manufacturing industry, and the advantages they offer are only partially exploited. The same is true for semantic technologies for simulations within production systems. Simulation serves as an essential tool for virtual manufacturing, but the required models are still created by experts in an often elaborate and mostly manual way. In this paper, we derive the requirements for semantic models to capture production simulation knowledge and present an ontological architecture for a knowledge graph for manufacturing and simulation that meets these requirements. The different ontologies of the architecture are based on established production and simulation standards, namely ISA-95 and VDI guidelines. By implementing the ISA-95 standard in an ontology, heterogeneous data from the production system can be integrated. Furthermore, the connection of ISA-95 models to simulation-specific concepts allows the representation of simulation models within the knowledge graph. The implementation of the knowledge graph is shown within a prototypical simulation framework.

Conversational Assistants like Alexa, Siri, and Google Assistant have become part of our everyday lives and faced wide adoption in industry. They support users in various tasks, such as playing songs or booking flights, making users' lives easier. Nevertheless, conversational assistants have not yet found their way into production, even though they would offer advantages, such as intuitive use through natural language and easy access to information. Simulation-based skills can also add a new layer in production by making simulation power accessible in an automated way. In this paper, we aim to discuss the concept of a simulation-based Conversational Assistant by presenting an architecture and the workflow within the architecture.

Recently, ontologies and semantic data technologies have increasingly come back into the focus of research due to the emerging use of knowledge graphs. However, even though the modeling and simulation community has recognized the potential of using this technology for the modeling process, for example for automatic model generation, adaptation or to represent simulation expert knowledge, a general and reusable approach for the aforementioned purposes is still missing. Therefore, in this paper a state of the art review for using knowledge representation during modeling and simulation processes of complex technical systems is conducted, such as factories or process plants with specific focus on the production and logistic domain. Based on that, requirements and benefits of knowledge graphs in this specific domain are evaluated.