Design Ontology Supporting Model-Based Systems Engineering Formalisms

Abstract

Model-based systems engineering (MBSE) provides an important capability for managing the complexities of system development. MBSE empowers the formalism of system architectures for supporting model-based requirement elicitation, specification, design, development, testing, fielding, etc. However, the modeling languages and techniques are heterogeneous, even within the same enterprise system, which leads to difficulties for data interoperability. The discrepancies among data structures and language syntaxes make information exchange among MBSE models more difficult, resulting in considerable information deviations when connecting data flows across the enterprise. Therefore, this article presents an ontology based upon graphs, objects, points, properties, roles, and relationships with extensions (GOPPRRE), providing metamodels that support the various MBSE formalisms across lifecycle stages. In particular, knowledge graph models are developed to support unified model representations to further implement ontological data integration based on GOPPRRE throughout the entire lifecycle. The applicability of the MBSE formalism is verified using quantitative and qualitative approaches. Moreover, the GOPPRRE ontologies are used to create the MBSE formalisms in a domain-specific modeling tool, MetaGraph, for evaluating its availability. The results demonstrate that the proposed ontology supports the formal structures and descriptive logic of the systems engineering lifecycle.

Exploring the Concept of Cognitive Digital Twins from Model-Based Systems Engineering Perspective

“…2. Ontology models are generated from KARMA models of auto-braking system architectures which represent the topology and information related to the MetaGraph models and Simulink models [41]. Thus, one set of system models refer to one entire 5 A model-driven approach for auto-braking system development [37] Step 1-3 KARMA Language supporting architecture design of auto-braking system [38] Step 1 KARMA Language supporting code generation for implement Simulink models automatically [39] Step 2 GOPPRRE ontology generation from KARMA language for constructing knowledge graph models [41] Step 4…”

“…Second, using a unified ontology, virtual entities, physical entities and their relationships can be formally described. KARMA language [38] and GOPPRRE ontology [41] provide a basic specification to develop architecture models. Metamodels, such as SysML diagrams, are developed under a unified semantic data structure which promotes the interoperability of architecture models.…”

Ontology-based system to support industrial system design for aircraft assembly

“…Heterogeneous MBSE modeling languages and techniques lead to discrepancies among data structures and language syntaxes, which complicates information exchange among MBSE models and results in difficulties for data interoperability. To describe system characteristics and system development based on meta-models for different architecture descriptions, a unified MBSE ontology is proposed that uses meta-meta models including Graph, Object, Point, Property, Role, and Relationship (GOPPRR) (Wang et al 2019), and in addition, the GOPPRRE with extensions (Lu et al 2020a).…”

“…Subclasses and individuals related to the application scenario are added accordingly to the IOF-Core ontology. They include the specific domain knowledge about the assembly process and correspond to the MBSE knowledge based on the GOPPRRE method (Lu et al 2020a). This application ontology serves as a semantic core in the proposed ontologybased system and is used to integrate information and knowledge of the requirements, architecture and behavioral models, and process specifications.…”

Digital Twin-Enabled Decision Support Services in Industrial Ecosystems

“…As shown in Figure 6, a real semantic modeling tool chain is proposed to support digital twin integration across the product lifecycle. An architecture modeling tool Meta-Graph 2.0 is used to develop architecture models for managing the complexity of the digital twins using a model-based systems engineering approach [43]. Through different models, architectural views of different digital twins are described using KARMA language.…”