This thesis is the combination of two research publications working towards automating functional modeling. Functional modeling is an underutilized yet critical tool for concept generation and product design. Understanding the difficulty both novice and expert designers have in implementing functional modeling in their design process, this research sets out to streamline the process of functional decomposition and help designers include functionality in their designs. Using existing consumer product data from a Design Repository database, we developed an algorithm to find correlations between component and function and flow, returning component-function-flow (CFF) combinations. The automation process organizes these connections by component-function-flow frequency (CFF frequency), thus allowing the creation of linear functional chains.
The first publication explores a preliminary method to automate the generation of linear functional chains using an Automated Frequency Calculation and Thresholding (AFCT) Algorithm. We use datasets of various scale and specificity to find correlations between functions and flows for components of products in the Design Repository. We use the results to predict the most likely functions and flows for a component, and then verify the accuracy of our algorithm by cross-validating a subsection of the data against the automation results. We then apply existing grammar rules to order the functions and flows in a linear functional chain.
The second publication describes the methodology used to develop a new metric, which we refer to as weighted confidence, to provide insight on the fidelity of the data returned by the above AFCT algorithm. In the previous publication, we found that CFF frequency is the best metric in formulating the linear functional chain for an individual component; however, we found that this metric did not account for prevalence and consistency in the Design Repository data. The weighted confidence metric is calculated by taking the harmonic mean of two metrics we extracted from our data, prevalence, and consistency.
Improving these automation results, allows us to further our ultimate objective of this research, which is to enable designers to automatically generate functional models for a product given constituent components.