A Computationally-assisted Methodology for Preference-guided Conceptual Design

摘要

We present a computationally-assisted methodology for capturing and incorporating preferences from designers into successive iterations of design concepts. An initial pool of creatively produced designs is parameterized and used in a computational search that recombines features to form new designs in a semi-automated way. Designs are evaluated quantitatively by performance calculations and evaluated qualitatively by human designers. Designer preference is captured when visual representations of designs are presented to the designer for subjective evaluation. The methodology searches for optimally performing designs, guided by quantitative performance models and designer preferences. The methodology couples the speed of computational searches with the ability of human designers to evaluate unmodeled objectives. The new methodology is demonstrated with a product example, which generates a set of designs that progressively improves in performance and designer preference. The proposed method brings the ability to generate numerous, optimally performing solutions across a wide solution space, in an efficient and human-centered way, and does so in the early stages of design.