Model-based systems engineering (MBSE) allows system models to formalize end-to-end systems engineering implementation while developing complex engineering system. The evolution of MBSE models, including changes and conflicts, provides important historical knowledge to support design decisions. Model versioning is an efficient approach to manage the evolution of MBSE models. However, the heterogeneous data structure and semantics used in MBSE practices hinder the tool interoperability that is required in model versioning, which also decreases the effectiveness and efficiency of system development. This paper proposes a tool-chain for model versioning of MBSE models based on a cognitive digital thread (CDT). In this tool-chain, the graph–object–point–property-relationship-role-extension (GOPPRR-E) modeling approach is adopted because it is compatible with heterogeneous modeling languages used in model versioning. To promote tool interoperability, this tool-chain adopts the Open Services for Lifecycle Collaboration to support conflict detection or resolution during model versioning. In particular, knowledge graphs are generated along with the model versioning workflow to develop a CDT, which provides the cognitive reasoning ability required for model versioning behaviors. A case study of landing gear system development is used to evaluate the feasibility of the proposed tool-chain through qualitative and quantitative analyses. The results demonstrate that the proposed tool-chain has better efficiency than traditional model versioning using Git tools.

The whole product life cycle consists of three phases: beginning of life (BOL), middle of life (MOL), and end of life (EOL). Although large amounts of product life-cycle data are generated over the whole product life cycle, data flows are rather vague after BOL. Over the last decade, however, emerging Internet, wireless mobile telecommunications, and product identification technologies have created the potential of making the whole product life cycle visible. As a result, the scope of data to be managed has expanded over the whole product life cycle. Hence, it becomes important to describe product life-cycle metadata in a systematic manner. Although much attention has been paid to data modeling over several objects such as products and processes, modeling methodology for product life-cycle metadata is not well developed. To cope with this limitation, we develop a modeling method for product life-cycle metadata by using the resource description framework (RDF). We define an RDF data model and its schema for describing and managing product life-cycle metadata. In addition, we describe how the proposed RDF model can be usefully applied to track, trace, and infer product life-cycle data with an RDF query language.