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.

With the increasing complexity of systems, model‐based systems engineering (MBSE) has attracted increasing attention in the industry. MBSE formalizes the whole lifecycles of products using models based on systems engineering aiming to improve the development efficiency of complex systems. Traditionally, MBSE approaches require many modeling languages in each phase of the entire lifecycle. Different syntax between such languages leads to difficulty in supporting an integrated description of transformations between models and data. Thus, it is challenged to utilize a general language to describe model formalism and transformation for architecture‐driven technology and code generation in one MBSE tool. In this paper, a multi‐architecture modeling language called Karma (introduced in Paper Part 1) is proposed to support the model transformations including architecture‐driven technology and code generation implementations in one modeling tool. Finally, from one auto‐braking case of an autonomous‐driving system, we find the availability of the Karma language supporting architecture‐driven technology and code generation is verified.

Currently, the fundamental tenets of systems engineering are supported by a model-based approach to minimize risks and avoid design changes in late development stages. The models are used to formalize, analyze, design, optimize, and verify system development and artifacts, helping developers integrate engineering development across domains. Although model-based development is well established in specific domains, such as software, mechanical systems, and electrical systems, its role in integrated development from a system perspective is still a challenge for industry. The model-based systems engineering (MBSE) tool-chain is an emerging technique in the area of systems engineering and is expected to become a next-generation approach for supporting model integration across domains. This article presents a literature review to highlight the usage and state of the art to generally specify the current understanding of MBSE tool-chain concepts. Moreover, the results are used for identifying the usage, advantages, barriers, concerns, and trends of tool-chain development from an MBSE perspective.

With the increasing complexity of aircraft development programs, the development processes of aircraft and their subsystems are continuously becoming complicated, leading to the growing risks of development cost across the entire life cycle. In this study, we proposed a model-based systems engineering approach to support process modeling of aircraft development using a multi-architecture modeling language KARMA. Simultaneously, property verification and hybrid automata simulation were used to implement the static cost analysis of each work task and dynamic cost analysis of the entire development process. Finally, a development process model of aircraft avionics system was created using a case study, in which cost analysis is implemented by the KARMA language. From the result, we found that the KARMA language enables the integration of the process modeling with static and dynamic analyses of the development process in a multi-architecture modeling tool MetaGraph 2.0.

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