The formalization of process knowledge using ontologies enables consistent modeling of parameter interdependencies in manufacturing. These interdependencies are typically represented as mathematical expressions that define relations between process parameters, supporting tasks such as calculation, validation, and simulation. To support cross-context application and knowledge reuse, such expressions are often defined in a generic form and applied across multiple process contexts. This highlights the necessity of a consistent and semantically coherent model to ensure the correctness of data retrieval and interpretation. Consequently, dedicated mechanisms are required to address key challenges such as selecting context-relevant data, ensuring unit compatibility between variables and data elements, and verifying the completeness of input data required for evaluating mathematical expressions. This paper presents a set of verification mechanisms for a previously developed ontology-based process model that integrates standardized process semantics, data element definitions, and formal mathematical constructs. The approach includes (i) SPARQL-based filtering to retrieve process-relevant data, (ii) a unit consistency check based on expected-unit annotations and semantic classification, and (iii) a data completeness check to validate the evaluability of interdependencies. The applicability of the approach is demonstrated with a use case from Resin Transfer Molding (RTM), supporting the development of machine-interpretable and verifiable engineering models.

Nowadays, companies face a variety of changes and challenges that often require adjustments and reconfiguration of manufacturing systems and processes. One topic of great importance in this regard are the interdependencies among process parameters. By modeling these interdependencies and making them accessible to experts and decision makers, these stakeholders can be supported in gaining a deeper understanding of production processes. This article presents a comprehensive overview of the oppor-tunities offered by a semantic model of parameter interdepen-dencies. Based on industrial standards, the semantic framework proposed aims to bridge the gap in current ontological models by providing a formal description of parameters, their interde-pendencies, and their effects on manufacturing processes. By leveraging the capabilities of Semantic Web technologies, this contribution describes the composition and decomposition of interdependency knowledge, its integration in process descriptions, and an interdependency data consistency check.

The following contribution introduces a concept that employs Large Language Models (LLMs) and a chatbot interface to enhance SPARQL query generation for ontologies, thereby facilitating intuitive access to formalized knowledge. Utilizing natural language inputs, the system converts user inquiries into accurate SPARQL queries that strictly query the factual content of the ontology, effectively preventing misinformation or fabrication by the LLM. To enhance the quality and precision of outcomes, additional textual information from established domain-specific standards is integrated into the ontology for precise descriptions of its concepts and relationships. An experimental study assesses the accuracy of generated SPARQL queries, revealing significant benefits of using LLMs for querying ontologies and highlighting areas for future research.

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.

The level of automation in production systems is increasing, with the use of advanced equipment to produce high-quality products. However, in today's dynamic situation, characterized by sustainability demands and shrinking product life cycles, manufacturing processes often require reconfiguration or even complete redesign. When undertaking the process reconfiguration or redesign of the production system, it is of utmost importance to explicitly describe the interdependencies among known parameters in order to anticipate the effects of parameter changes accurately. In this contribution, an approach for integrating process parameter interdependencies into a semantic model is presented. The semantic model uses various so-called Ontology Design Patterns. These patterns contain terminologies and logic of industrial standards. Models following this approach enable a support for process redesign by representing semantically enriched process knowledge. In addition, the model can be utilized to perform calculations and generate process output data based on specific process input data.

Simulations play a crucial role in the (re)design of manufacturing processes, serving as virtual testing grounds. Specific simulations are used to describe different parts of production processes and the associated production systems. Often a single simulation alone is not sufficient to test specific process settings, but a combination of several simulations is needed. One simulation may require input parameters that are other simulations’ outputs. This means that oftentimes simulation sequences are necessary to answer specific questions.The manual planning of these sequences causes high efforts because factors such as time, cost and quality must be taken into account when selecting the optimal simulation scenario. This paper identifies and discusses challenges involved in creating simulation sequences. Moreover, it identifies the requirements for the implementation of a concept for the automated generation of such sequences. Additionally, an initial draft of the concept is presented.