Identifying High-Value CAD Models: An Exploratory Study on Dimensional Variability As Complexity Indicator

Digital product data quality has proven to be a unifying theme in designing and reusing efficient products, particularly in the context of the Model-Based Enterprise (MBE). More specifically, the quality of the master model (usually a history-based parametric model) is critical, as it determines the quality of all secondary models used in subsequent downstream processes. However, no quantitative metrics exist that can provide a reliable assessment of quality at a high semantic level. In this paper, we introduce dimensional variability as a quality indicator for parametric models that connects the effective variability range of the dimensional constraints in a model to the robustness and flexibility of the parametric geometry, which determines its reusability. As a validation effort, we report the results of a study where a set of parametric models of varying complexity was analyzed, and discuss the significance of the links between the proposed metric and various aspects of the internal graph structure of the CAD model.