UNIFICATION OF STYLISTIC FORM AND FUNCTION
Great design is often the result of intelligent balancing of tradeoffs and leveraging of synergies between multiple product goals. While the engineering design community has numerous tools for managing the interface between functional goals in products, there are currently no formalized methods to concurrently manage stylistic form and functional requirements. The purpose of the work in this dissertation is to formalize ways to coordinate seemingly disparate but highly related goals of stylistic form and functional constraints in both computational design and for human designers. This work aims to provide a cohesive framework where both computational and cognitive findings are brought together to mutually inform and inspire the design process.
First, this problem was approached computationally with the development of an Artificial Neural Network based machine learning system that allows consumer judgments of stylistic form to be modeled quantitatively. Coupling this quantitative model of stylistic form with a Genetic Algorithm enables computers to concurrently account for multiple objectives in the domains of stylistic form and function within the same quantitative framework. This coupling then opens the door for computers to automatically generate products that not only work well, but also look good doing it.
Second, this problem was approached cognitively to explore ways to help human designers manage different goals of stylistic form and function more efficiently and effectively. An experiment was conducted which suggests that designers may sometimes have trouble fully utilizing knowledge they already have for managing different goals in design problems. This experiment shows evidence that analogical inspiration can help designers to overcome this knowledge block to more intelligently balance tradeoffs and leverage synergies in engineering design.
Primary Researcher: IAN TSENG