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.

This paper presents a SysML profile that enables the direct integration of planning semantics based on the Planning Domain Definition Language (PDDL) into system models. Reusable stereotypes are defined for key PDDL concepts such as types, predicates, functions and actions, while formal OCL constraints ensure syntactic consistency. The profile was derived from the Backus-Naur Form (BNF) definition of PDDL 3.1 to align with SysML modeling practices. A case study from aircraft manufacturing demonstrates the application of the profile: a robotic system with interchangeable end effectors is modeled and enriched to generate both domain and problem descriptions in PDDL format. These are used as input to a PDDL solver to derive optimized execution plans. The approach supports automated and model-based generation of planning descriptions and provides a reusable bridge between system modeling and AI planning in engineering design.

Model-based systems Engineering (MBSE) approaches can be applied in the early engineering phases of the development of production systems. They help the system engineer to determine the suitability of production systems and assist in design decisions. To achieve this in a structured manner, several Domain-Specific Modeling Languages (DSMLs) were developed in this contribution using the UML profile mechanism and then integrated into an MBSE workflow. These DSMLs are based on industry standards which consider the Product, Process, and Resource (PPR) structures, allowing consistent modeling while considering the system variability. The application of the DSMLs is demonstrated through the development of a highly automated production system for aircraft fuselage components. Additionally, this contribution showcases how different implementation variants can be compared through simulation.

Manually creating Planning Domain Definition Language (PDDL) descriptions is difficult, error-prone, and requires extensive expert knowledge. However, this knowledge is already embedded in engineering models and can be reused. Therefore, this contribution presents a comprehensive workflow for the automated generation of PDDL descriptions from integrated system and product models. The proposed workflow leverages Model-Based Systems Engineering (MBSE) to organize and manage system and product information, translating it automatically into PDDL syntax for planning purposes. By connecting system and product models with planning aspects, it ensures that changes in these models are quickly reflected in updated PDDL descriptions, facilitating efficient and adaptable planning processes. The workflow is validated within a use case from aircraft assembly.

Contrary to classic document-based engineering, model-based systems engineering (MBSE) is an approach that focuses on the definition of engineering artifacts as formal information models. In complex engineering projects, such as the design of automated aircraft production systems, MBSE is a means to control complexity and support interdisciplinary collaboration. To implement MBSE in industry, ready-to-use modeling workflows that guide and support system engineers are required to minimize overhead workload. System Modeling Language (SysML), a general purpose modeling language derived from Unified Modeling Language (UML), is widely accepted as the standard modeling language in MBSE and therefore supported by industry-leading software. In this paper, we propose a SysML-based modeling workflow that supports the conceptual engineering of automated production systems. The system engineer is guided through a multi-layer modeling workflow, which is based on the Software Platform Embedded Systems (SPES) method. The modeling workflow is validated by applying it in the engineering of an automated production system that is used in aircraft manufacturing.

Shorter product lifecycles and increasing product complexity are important trends that drive the need for increased flexibility of production companies. One way to cope with this problem is to engage in flexible production networks where companies can find missing production capabilities and offer their own ones. To enable this connection across company borders and software platforms, an agent-based communication approach is proposed that is based on the OPC UA standard. This paper also develops an optimization concept that can increase a company's benefits, i.e. profits, by applying a learning algorithm as well as an approach to calculate optimal margins, which is encapsulated in an optimization agent.