Inverse Parametric Modeling From Independently Generated Product Data Sets

Parametric modeling has become a widely accepted mechanism for generating data set variants for product families. These data sets that include geometric models and feature-based process plans are created by specifying values for parameters within feasible ranges specified as constraints in the definition. The ranges denote the extent or envelope of the product family. Increasingly, with globalization the inverse problem is becoming important. This takes independently generated product data sets that on observation belong to the same product family and creates a parametric model for that family. This problem is also of relevance to large companies where independent design teams may work on product variants without much collaboration only to attempt consolidation later on to optimize the design of manufacturing processes and systems. In this paper we present a methodology for generating a feature-based part family parametric model through merging independently generated product data sets. We assume that these data sets are feature-based with relationships such as precedences captured using graphs. Since there are typically numerous ways in which these data sets can be merged, we formulate this as an optimization problem and solve using the A* algorithm. The parameter ranges generated by this approach will be used to design appropriate Reconfigurable Machine Tools (RMTs) and systems (RMS) for manufacturing the resulting part family.