Including production considerations in the early design stages of aircraft structures is challenging. Production information is mostly known by experts and rarely formally documented such that it can be effectively used during the design process. Producibility is mostly considered after completing the design, resulting in increased cost and development time due to the late discovery of production issues. This paper presents a new model, called the Manufacturing Information Model (MIM), which supports the automatic inclusion of production considerations into the design process. The MIM provides a single source of truth and a generic structure to capture and organize production-related information in a product system. Furthermore, it provides compatibility analyses to automatically warn for or exclude infeasible designs. Analysis tools use the information stored within the MIM to calculate the mass, costs, and production rate of the product. To show the functionalities of the MIM, it has been applied to the conceptual design of a wing box at a Tier 1 company. This use case shows how the MIM supports trade-off decisions, as it allows for the identification of trends and the ranking of different manufacturing concepts. Overall, the MIM provides a structured and formal approach to include production information in the conceptual design, improving the decision-making process.

This paper discusses the approach for architecture design space optimization of aeronautical systems investigated in the DEFAINE project. In system architecture optimization problems, hierarchical relations between design variables may exist. This means that the quantity and type of some variables are dependent on the value of other variables. To address this challenge, a nested optimization strategy is proposed, where an outer loop deals with the independent design variables and an inner loop with the dependent ones. As the characteristics of the dependent variables may become known only during workflow execution, a dynamic workflow formulation approach is developed to introduce these variables into the MDAO workflow, automatically, during execution. The proposed methodology is implemented using a suite of technologies provided by partners of the DEFAINE consortium to enable formulation and execution of collaborative MDAO problems. This implementation is then applied to the design and optimization of the structural layout of a UAV aileron. The goal is to minimize the aileron mass, subjected to failure criteria constraints. In this use case, hierarchical relations between variables are present. The aileron skin is discretised into material zones, each one defined by a (dependent) design variable. The number of zones, hence the number of design variables, is not known a priori as it depends on the number and position of ribs and spars(both independent variables). A preliminary implementation of the proposed approach proved effective in dealing with the hierarchical mixed-integer design space. Trade off studies comparing different aileron architectures could be efficiently set up and executed. Some limitations in the implemented workflow management technology, however prevent performing a full nested architecture optimization. Therefore, to demonstrate the effectiveness of the proposed nested approach, a parallel study was conducted on the same aileron, using a less generic and manual implementation of the nested MDAO workflow. This second study successfully minimized the mass and cost of the aileron structure, thereby proving the merit of the proposed nested approach to address system architecture optimization.