Título
How Does the Modeling Strategy Influence Design Optimization and the Automatic Generation of Parametric Geometry Variations?Otras instituciones
Universitat Politècnica de València (UPV)Purdue University
Versión
Postprint
Derechos
© 2022 ElsevierAcceso
Acceso embargadoVersión del editor
https://doi.org/10.1016/j.cad.2022.103364Publicado en
Computer-Aided Design Vol. 151. Artículo 103364. October, 2022Editor
ElsevierPalabras clave
CAD reusabilityParametric modeling methodologies
Design intent
CAD quality
Resumen
The robustness and flexibility of a feature-based parametric CAD model determines the extent to which the geometry can be modified and reused in other design scenarios. The ability of a model to succe ... [+]
The robustness and flexibility of a feature-based parametric CAD model determines the extent to which the geometry can be modified and reused in other design scenarios. The ability of a model to successfully adapt to changes depends on the type and sequence of the modeling operations selected to build the geometry, the parent–child dependencies defined during the modeling process, and the type and scope of the desired geometric change. Several formal modeling methodologies have been proposed to maximize model reusability, which have been shown to outperform unstructured approaches when designers need to manually modify the geometry. However, the effect of these parametric model strategies on the generation of valid solutions in heavily automated tasks has not yet been investigated. In this paper, we compare and analyze the performance of three well-established parametric modeling methodologies in various design optimization scenarios that involve the automatic generation of a large number of geometric variations. We discuss the results of a study with four parametric models of varying complexity and identify the limitations of each strategy in relation to the internal structure of the model. Our results show that explicit references and resilient modeling strategies are relatively robust for simple parts, but their effectiveness decreases significantly as the complexity of the model increases. In addition, we introduce the concept of intrinsic variability, which impacts the effectiveness of the methodology, and thus the quality of the parametric model, based on how the methodology is interpreted and executed. [-]
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