In the life cycle of production systems, effectiveness and efficiency of engineering have become increasingly important. Along the complete engineering chain, both qualities are strongly related to the management of engineering data consistency. Consistency and configuration management require representing dependencies in the engineering data, specifically between engineering objects and their properties. While the open AutomationML standard provides capabilities to represent interfaces and dependencies within an engineering object, it remains open how to represent general consistency or configuration dependencies between different engineering objects and their properties. In this paper, we address dependency modelling for engineering data of an AutomationML based engineering tool chain as a representative example of engineering chains. We introduce a general dependency representation for engineering objects and their properties in AutomationML. We evaluate the dependency representation capabilities in a use case and derive a research agenda.

Almost everyone knows the saying “never change a running system” which is especially true for running production facilities. Nevertheless, in order to remain competitive in the future, today's companies must change their production strategy towards Industry 4.0. The question therefore arises: How can such a change of strategy are managed in a structured way in the brownfield? Based on the analysis of scientific findings and the validation on two practical application cases, this paper provides a reference architecture and a guideline to adapt existing facilities towards Industry 4.0.

Efficient and effective engineering data exchange is increasingly considered a key success factor in the life cycle of production systems, leading to the intensified development of data logistic solutions. As small and medium-size companies (SMEs) play important roles in modern engineering organization structures, SMEs have to improve their capabilities to take part in these data logistics solutions. Unfortunately, SMEs have strong human and financial resource limitations. In this paper, we introduce a modular and easy-to-use data logistics architecture that aims at enabling SMEs to implement proof-of-concept software structures, applicable to validate benefits and challenges of data logistic solutions. This data logistics architecture provides a migration path towards the full participation of SMEs in data logistic solutions for engineering data exchange. We demonstrate the application of the architecture on use cases in automotive, steel, and machining industries.

Production system engineering involves various engineering disciplines and tools within an engineering organization. Digitalization in such an engineering organization strongly depends on the quality of the data logistics that integrates the domain-specific languages applied in and between the engineering disciplines. In this paper, we introduce a meta model and method for identifying common concepts, the common elements of the involved disciplines, for establishing the foundation for data logistics. The Common Concepts Identification method elicits common concepts on pathways through the engineering process and, thereby, facilitates designing an optimized data logistics for the digitalization in engineering.

Advanced production systems, for example in the European automotive or steel industries, incorporate Industry 4.0 defined structures based on Industry 4.0 components and their asset administration shells. Several development and standardization initiatives define structures, behavior patterns, meta models, etc. These initiatives assume the Industry 4.0 asset administration shell (I4.0AAS) as a digital twin of the Industry 4.0 component to contain all relevant engineering and runtime data. However, there is only limited discussion on how to collect and represent these data effectively and efficiently. In this paper, we discuss the collection and representation of engineering data as part of the I4.0AAS. We introduce the I4.0AAS completion method to facilitate the collection of the I4.0AAS engineering data set and its export to an I4.0AAS serialization. In the multi-disciplinary engineering of production systems, the I4.0AAS completion method builds on the AutomationML standard and on the data logistics between engineering workgroups. We evaluate the I4.0AAS completion method in a feasibility study with an I4.0 measurement cell.