The goal of this research is to propose a data mining based design framework that can be used to solve complex systems design problems in a timely and efficient manner, with the main focus being product family design problems.Traditional data acquisition techniques that have been employed in the product design community have relied primarily on customer survey data or focus group feedback as a means of integrating customer preference information into the product design process. The reliance of direct customer interaction can be costly and time consuming and may therefore limit the overall size and complexity of the customer preference data. Furthermore, since survey data typically represents stated customer preferences (customer responses for hypothetical product designs, rather than actualproduct purchasing decisions made), design engineers may not know the true customer preferences for specific product attributes, a challenge that could ultimately result in misguided product designs. By analyzing large scale time series consumer data, new products can be designed that anticipate emerging product preference trends in the market space. The proposed data trend mining algorithm will enable design engineers to determine how to characterize attributesbased on their relevance to the overall product design. A cell phone case study is used to demonstrate product designproblems involving new product concept generation and an aerodynamic particle separator case study is presentedfor product design problems requiring attribute relevance characterization and product family clustering. Finally, itis shown that the proposed trend mining methodology can be expanded beyond product design problems to include systems of systems design problems such as military systems simulations.
PDF
download
878987797
028-85220240
