Triple-Helix Structured Model Based on Problem-Knowledge-Solution Co-evolution for Innovative Product Design Process

Abstract

A design problem with deficient information is generally described as wicked or ill-defined. The information insufficiency leaves designers with loose settings, free environments, and a lack of strict boundaries, which provides them with more opportunities to facilitate innovation. Therefore, to capture the opportunity behind the uncertainty of a design problem, this study models an innovative design as a composite solving process, where the problem is clarified and resolved from fuzziness to satisfying solutions by interplay among design problems, knowledge, and solutions. Additionally, a triple-helix structured model for the innovative product design process is proposed based on the co-evolution of the problem, solution, and knowledge spaces, to provide designers with a distinct design strategy and method for innovative design. The three spaces interact and co-evolve through iterative mappings, including problem structuring, knowledge expansion, and solution generation. The mappings carry the information processing and decision-making activities of the design, and create the path to satisfying solutions. Finally, a case study of a reactor coolant flow distribution device is presented to demonstrate the practicability of this model and the method for innovative product design.

Abstracting and formalising the design co-evolution model

“…In design research, concepts of co-evolution have almost exclusively been used to describe how problems and solutions change, and how the relationship between them changes too. There have been occasional suggestions that an additional category could be added, such as audience (Halstrøm & Galle 2015) or knowledge (Hui et al 2020). There have also been suggestions that subcategories of problems and solutions could be defined by considering problem-solution variants, such as function, behaviour and structure (Maher & Tang 2003), knowledge and rules 14/23 (Yu et al 2015), or analysis, synthesis and evaluation (Martinec et al 2020).…”

“…In considering the interactions between an architect and client, Smulders, Reyman & Dorst (2009) equate problems to functions, and solutions to structures, but also add another layer of ‘use’ which they equate to behaviour, citing Gero’s (1990) function behaviour structure (FBS) ontology. In similar theoretical expansions, Halstrøm & Galle (2015) describe the co-evolution of problem, solution and audience, and Hui et al (2020) describe the co-evolution of problem, solution and knowledge. Crilly & Moroşanu Firth (2019) more generally promote the idea of an ‘ecology’ of co-evolving ideas, including ideas about problems and solutions, but also ideas about design processes, users and application domains.…”

Solution Evolution Knowledge Service Based on Design Iteration in Strain Sensor Design

“…Therefore, Gero et al proposed a situated Function–Behavior–Structure (sFBS) model of co-design based on the FBS theory that describes the representation of overall co-design activities while retaining a fine-grained representation of each designer’s interaction with their co-designers and their internal cognitive processes [ 38 ]. This research team proposed a triple-helix structured model based on problem–knowledge–solution co-evolution for innovative product design process [ 39 ], which provides designers with unique innovation design strategies and methods. However, the triple-helix structured model and sFBS theory consider less the role of design strategies in linking design problems and design knowledge.…”

Domain Reconstruction and Mutation Approach to Expand Creative Design Solution Space for Practical Mechanical Systems

“…Selec Scheme 2, { [ the left Fig. ( the database the function-f h t h e h e u r i 6, 42], or oach [7,20] The subset ) , The rocker in the crank-rocker mechanism can provide the one-dwell motion, S ( 6 b ), determined by its inherent characteristics when the specifically shaped coupling curve on the cam's follower drives the crank-rocker mechanism. It can be found that the combined mechanism is no more complicated than other matched mechanisms related to the subset ) , , ( .…”

“…For routine design problems [13], the main task of the computerized conceptual design system based on functional synthesis approaches is to implement the search and mapping between functions and physical structures, which results in a relatively small solution space and weakens the scheme's innovativeness [14][15][16]. Restricted by the complexity and uncertainty of reasoning processes and the incompleteness of reasoning knowledge [17][18][19][20], the AI-based FBS model for function reasoning mainly deals with the conceptual design of mechanical systems in series combinations [21][22][23][24] but fails to support non-routine design problems [13,25]. To overcome the flaws ascribed to classical function-based methods and increase the FBS model's capability to generate creative design schemes, the integrated framework [26,27] to manage abstraction levels…”