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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. 

Abstract The objective of this research is to support DfX considerations in the early phases of design. In order to do conduct DfX, designers need access to pertinent downstream knowledge that is keyed to early stage design activities and problem knowledge. Product functionality is one such “key” connection between early understanding of the design problem and component choices which dictate product performance and impact, and repositories of design knowledge are one way to archive such design knowledge. However, curation of design knowledge is often a time-consuming activity requiring expertise in product modeling. In this paper, we explore a method to automate the populating of design repositories to support the overall goal of having up-to-date repositories of product design knowledge. To do this, we mine information from an existing repository to better understand the relationships between the components, functions, and flows of products. The resulting knowledge can be applied to automate functional decompositions once a product's components have been entered and thus reliably provide that “key” between early design activities and the later, component dependent characteristics. 

During the design process, designers must satisfy customer needs while adequately developing engineering objectives. Among these engineering objectives, human considerations such as user interactions, safety, and comfort are indispensable during the design process. Nevertheless, traditional design engineering methodologies have significant limitations incorporating and understanding physical user interactions during early design phases. For example, Human Factors methods use checklists and guidelines applied to virtual or physical prototypes at later design stages to evaluate the concept. As a result, designers struggle to identify design deficiencies and potential failure modes caused by user-system interactions without relying on the use of detailed and costly prototypes. The Function-Human Error Design Method (FHEDM) is a novel approach to assess physical interactions during the early design stage using a functional basis approach. By applying FHEDM, designers can identify user interactions required to complete the functions of the system and to distinguish failure modes associated with such interactions, by establishing user-system associations using the information of the functional model. In this paper, we explore the use of data mining techniques to develop relationships between component, functions, flows and user interactions. We extract design information about components, functions, flows, and user interactions from a set of distinct coffee makers found in the Design Repository to build associations rules. Later, using a functional model of an electric kettle, we compared the functions, flows, and user interactions associations generated from data mining against the associations created by the authors, using the FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We are suggesting that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions. This work will contribute to the design community by automating the identification of user interactions from a functional model.