Evaluation of a Model-based Approach for the Development of Production Systems in Aeronautics

Abstract

Current production rates cannot meet the growing demand for present and future aircraft. To achieve the required ramp-up, engineers must ensure early in the development process that aircraft production systems meet all industrial requirements. This necessitates the concurrent optimization of both product design and production system planning, emphasizing the need for early integration between the two. Currently, high-fidelity modeling approaches, such as Digital Factory and Product-Process-Resource (PPR) frameworks, are used for collaborative development. However, these methods involve time-intensive modeling processes, high computational demands for simulations, and challenges in managing data. To address these issues, executable Model-Based Systems Engineering (MBSE) models can support production system modeling in the early conceptual stages of development. These abstract models not only enable production system assurance but also facilitate seamless interoperability with product models. This paper focuses on extending an RFLP-based (Requirements, Functional, Logical, Physical) methodology previously introduced by the authors to support the concurrent development of production systems and the optimization of product designs for production purposes. A domain-specific language is proposed to model production artifacts and establish interdependencies with product design. By leveraging libraries for flexible, bottom-up model development, this approach ensures modularity and efficiency. Additionally, executable model artifacts facilitate the simulation of the overall production system and the validation of industrial requirements. The application of the methodology and the key outcomes of this research demonstrate the benefits of MBSE in early development stages, including enhanced traceability and the assurance of requirements. Consequently, a broader design space can be explored, enabled by increased reusability and automation potential, as well as improved complexity management. This methodology provides a solid foundation for addressing the challenges of future aircraft production through earlier integration of product and production development