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

Volume 3: Manufacturing Equipment and Systems

Abstract

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 inte...

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