The Bisociative Design framework proposed in this work aims to quantify hidden, previously unknown design synergies/insights across seemingly unrelated product domains. Despite the overabundance of data characterizing the digital age, designers still face tremendous challenges in transforming data into knowledge throughout the design processes. Data driven methodologies play a significant role in the product design process ranging from customer preference modeling to detailed engineering design. Existing data driven methodologies employed in the design community generate mathematical models based on data relating to a specific domain and are therefore constrained in their ability to discover novel design insights beyond the domain itself (I.e., cross domain knowledge).
The Bisociative Design framework proposed in this work overcomes the limitations of current data driven design methodologies by decomposing design artifacts into form patterns, function patterns and behavior patterns and then evaluating potential cross-domain design insights through a proposed multidimensional Bisociative Design metric.
A hybrid marine model involving multiple domains (capable of flight and marine navigation) is used as a case study to demonstrate the proposed Bisociative Design framework and explain how associations and novel design models can be generated through the discovery of hidden, previously unknown patterns across multiple, unrelated domains.
Pose-on-the-Go: Approximating User Pose with Smartphone Sensor Fusion and Inverse Kinematics
