A ReliefF attribute weighting and X-means clustering methodology for top-down product family optimization

Abstract

This article proposes a top-down product family design methodology that enables product design engineers to identify the optimal number of product architectures directly from the customer preference data set by employing data mining attribute weighting and clustering techniques. The methodology also presents an efficient component sharing strategy to aid in product family commonality decisions. Two key data mining models are presented in this work to help guide the product design process: (1) the ReliefF attribute weighting technique that identifies and ranks product attributes, and (2) the X-means clustering approach that autonomously identifies the optimal number of candidate products. Product family commonality decisions are guided by once again employing the X-means clustering technique, this time to identify the components across product families that are most similar. A family of prototype aerodynamic air particle separators is used to evaluate the efficiency and validity of the proposed product family design methodology.