Supporting designers is one of the main motivations for design research. However, there is an ongoing debate about the ability of design research to transfer its results, which are often provided in form of design methods, into practice. This article takes the position that the transfer of design methods alone is not an appropriate indicator for assessing the impact of design research by discussing alternative pathways for impacting design practice. Impact is created by different means – first of all through the students that are trained based on the research results including design methods and tools and by the systematic way of thinking they acquired that comes along with being involved with research in this area. Despite having a considerable impact on practice, this article takes the position that the transfer of methods can be improved by moving from cultivating method menageries to facilitating the evolution of method ecosystems. It explains what is understood by a method ecosystem and discusses implications for developing future design methods and for improving existing methods. This paper takes the position that efforts on improving and maturing existing design methods should be raised to satisfy the needs of designers and to truly support them.

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.

Additive manufacturing (AM) is becoming increasingly attractive for aerospace companies due to the fact of its increased ability to allow design freedom and reduce weight. Despite these benefits, AM comes with manufacturing constraints that limit design freedom and reduce the possibility of achieving advanced geometries that can be produced in a cost-efficient manner. To exploit the design freedom offered by AM while ensuring product manufacturability, a model-based design for an additive manufacturing (DfAM) method is presented. The method is based on the premise that lessons learned from testing and prototyping activities can be systematically captured and organized to support early design activities. To enable this outcome, the DfAM method extends a representation often used in early design, a function–means model, with the introduction of a new model construct—manufacturing constraints (Cm). The method was applied to the redesign, manufacturing, and testing of a flow connector for satellite applications. The results of this application—as well as the reflections of industrial practitioners—point to the benefits of the DfAM method in establishing a systematic, cost-efficient way of challenging the general AM design guidelines found in the literature and a means to redefine and update manufacturing constraints for specific design problems.

One problem in incremental product development is that geometric models are limited in their ability to explore radical alternative design variants. In this publication, a function modeling approach is suggested to increase the amount and variety of explored alternatives, since function models (FM) provide greater model flexibility. An enhanced function-means (EF-M) model capable of representing the constraints of the design space as well as alternative designs is created through a reverse engineering process. This model is then used as a basis for the development of a new product variant. This work describes the EF-M model's capabilities for representing the design space and integrating novel solutions into the existing product structure and explains how these capabilities support the exploration of alternative design variants. First-order analyses are executed, and the EF-M model is used to capture and represent already existing design information for further analyses. Based on these findings, a design space exploration approach is developed. It positions the FM as a connection between legacy and novel designs and, through this, allows for the exploration of more diverse product concepts. This approach is based on three steps – decomposition, design, and embodiment – and builds on the capabilities of EF-M to model alternative solutions for different requirements. While the embodiment step of creating the novel product's geometry is still a topic for future research, the design space exploration concept can be used to enable wider, more methodological, and potentially automated design space exploration.