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Design space exploration and margin analysis can inform critical decisions early in engineering design, helping to handle the uncertainties of early design while ensuring design performance. In practice, the complexity of many products makes such decision-making challenging. This paper addresses the challenge with a new design framework that relies on the margin value method to evaluate sets of concepts that are combinatorially generated from an enhanced function-means tree. The basis for concept comparison is the margin value in each design alternative. The margin value method is expanded to address a broad class of design problems by using surrogate models and novel metrics for evaluating different conceptual alternatives. Visualization tools are introduced to support the evaluations. The efficacy of the framework is demonstrated using the design of a structural aero-engine component involving simulation models and uncertain load specifications. Overall, this paper shows how design concepts can be compared objectively and distilled to a set of alternatives that would retain their values throughout product development. [ABSTRACT FROM AUTHOR]

Requirement changes can result in substantial overdesign because of the way design margins are allocated at the beginning of the design process. We present a design optimization method for minimizing overdesign by making use of additive remanufacturing and recently defined constituents of design margins (buffer and excess). The method can be used to obtain a set of design decisions for different changing requirement scenarios. We demonstrate our method by means of a turbine rear structure design problem where changes in the temperature loads are met by depositing different types of stiffeners on the outer casing. The results of the case study are visualized in a tradespace, which allows for comparison between sets of optimal, flexible, and robust designs. Results show that the optimized set of design decisions balances flexibility and robustness in a cost-effective manner. [ABSTRACT FROM AUTHOR]

Design requirements are often uncertain in the early stages of product development. Set-based design is a paradigm for exploring, and keeping under consideration, several alternatives so that commitment to a single design can be delayed until requirements are settled. In addition, requirements may change over the lifetime of a component or a system. Novel manufacturing technologies may enable designs to be remanufactured to meet changed requirements. By considering this capability during the set-based design optimization process, solutions can be scaled to meet evolving requirements and customer specifications even after commitment. Such an ability can also support a circular economy paradigm based on the return of used or discarded components and systems to working condition. We propose a set-based design methodology to obtain scalable optimal solutions that can satisfy changing requirements through remanufacturing. We first use design optimization and surrogate modeling to obtain parametric optimal designs. This set of parametric optimal designs is then reduced to scalable optimal designs by observing a set of transition rules for the manufacturing process used (additive or subtractive). The methodology is demonstrated by means of a structural aeroengine component that is remanufactured by direct energy deposition of a stiffener to meet higher loading requirements. [ABSTRACT FROM AUTHOR]