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.

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.

During the design process, user considerations such as usability, safety, and comfort should be adequately developed by designers. Nevertheless, traditional design engineering methodologies have limitations to incorporate human factors engineering principles during early design phases. Common human factors methods utilize virtual or physical prototypes at later design stages to assess the user's interaction with the design concept. As a result, designers rely on detailed and costly prototypes to identify design deficiencies and potential failure modes caused by user-system interactions. The Function-Human Error Design Method (FHEDM) is a human-centered approach to assess physical interactions and distinguish failure modes associated with such interactions by establishing user-system associations using the functional model's information. In this work, we explore data mining techniques to automate the construction of relationships between components, 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 association 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 authors' associations using FHEDM. The results show notable similarities between the associations built from data mining and the FHEDM. We suggest that design information from a rich dataset can be used to extract association rules between functions, flows, components, and user interactions.

The goal of this research is to characterize the effects of use patterns on the environmental sustainability of consumer products, and to enable decision making throughout design processes that encourages product sustainability. Life Cycle Assessments (LCA) are currently used to evaluate the environmental impact of a product, but there can be considerable uncertainty in these analyses, especially relating to the use phase of the product. To better understand this uncertainty, we conducted environmental impact assessments of 20 household products, and employed two uncertainty quantification approaches to accommodate variation in the use phase of these products. The results from each product were then compared to products with similar attributes to find generalizations. This knowledge was integrated into decision trees so designers can better understand the degree to which use-phase uncertainty can affect quantitative measures of environmental impact before performing LCAs. This work enables designers to make more informed decisions about the intended use and use lifetimes of consumer products, potentially leading to a reduced environmental impact of this life cycle phase.

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