A product family design methodology employing pattern recognition
Freeman, Dane Fletcher
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Sharing components in a product family requires a trade-off between the individual products' performances and overall family costs. It is critical for a successful family to identify which components are similar, so that sharing does not compromise the individual products' performances. This research formulates two commonality identification approaches for use in product family design and investigates their applicability in a generic product family design methodology. Having a commonality identification approach reduces the combinatorial sharing problem and allows for more quality family alternatives to be considered. The first is based on the pattern recognition technique of fuzzy c-means clustering in component subspaces. If components from different products are similar enough to be grouped into the same cluster, then those components could possibly become the same platform. Fuzzy equivalence relations that show the binary relationship from one products' component to a different products' component can be extracted from the cluster membership functions. The second approach builds a Bayesian network representing the joint distribution of a design space exploration. Using this model, a series of inferences can be made based on product performance and component constraints. Finally the posterior design variable distributions can be processed using a similarity metric like the earth mover distance to identify which products' components are similar to another's.