Using Decision Trees Supported by Data Mining to Improve Function-Based Design

Abstract

Engineering designers currently use downstream information about product and component functions to facilitate ideation and concept generation of analogous products. These processes, often called Function-Based Design, can be reliant on designer definitions of product function, which are inconsistent from designer to designer. In this paper, we employ supervised learning algorithms to reduce the variety of component functions that are available to designers in a design repository, thus enabling designers to focus their function-based design efforts on more accurate, reduced sets of potential functions. To do this, we generate decisions trees and rules that define the functions of components based on the identity of neighboring components. The resultant decision trees and rulesets reduce the number of feasible functions for components within a product, which is of particular interest for use by novice designers, as reducing the feasible functional space can help focus the design activities of the designer. This reduction was evident in both case studies: one exploring a component that is known to the designer, and the other looking at defining function of an unrecognizable component. The work presented here contributes to the recent popularity of using product data in data-driven design methodologies, especially those focused on supplementing designer cognition. Importantly, we found that this methodology is reliant on repository data quality, and the results indicate a need to continue the development of design repository data schemas with improved data consistency and fidelity. This research is a necessary precursor for the development of function-based design tools, including automated functional modeling.