Common design structures and substitutable feature discovery in CAD databases

Vasantha, Gokula and Purves, David and Quigley, John and Corney, Jonathan and Sherlock, Andrew and Randika, Geevin (2021) Common design structures and substitutable feature discovery in CAD databases. Advanced Engineering Informatics, 48. 101261. ISSN 1474-0346

[thumbnail of Vasantha-etal-AEI-2021-Common-design-structure-substitutable-feature-discovery-in-CAD-databases]
Preview
Text (Vasantha-etal-AEI-2021-Common-design-structure-substitutable-feature-discovery-in-CAD-databases)
Vasantha_etal_AEI_2021_Common_design_structure_substitutable_feature_discovery_in_CAD_databases.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo

Download (10MB)| Preview

    Abstract

    It has been widely reported that the reuse of previously created components, or features, in new engineering designs will improve the efficiency of a company's product development process. Although the reuse of engineering components has established metrics and methodologies, the reuse of specific design features (e.g. stiffening ribs, hole patterns or lubrication grooves, etc.) has received less attention in the literature. Typically, researchers have reported approaches to partial design reuse that identify patterns predominately in terms of geometrically similar shapes (i.e. a set of features) whose elements are adjacent, cohesive, and decoupled from the overall form of a component. In contrast, this paper defines a common design structure (CDS) as collections of frequently occurring features (e.g. holes) with common parametric values (e.g. diameters) in a CAD database (irrespective of their locations or spatial connectivity between other features on a component). By exploiting the established data-mining technology of association rules and item-sets the authors show how CDSs can be efficiently computed for hundreds of 3D CAD models. A case study, with hole data extracted from a publicly available dataset of hydraulic valves, is presented to illustrate how item-sets associated with CDS can be computed and used to support predictive design by identifying potentially 'substitutable features' during an interactive design process. This is done using a combination of association rules and geometric compatibility checks to ensure the system’s suggestion are implementable. The use of the Kullback–Leibler divergence to assess the degree of similarity between components is identified as a crucial step in the process of identifying the "best" suggestions. The results illustrate how the prototype implementation successfully mines the CDSs and identifies substitutable hole features in a dataset of industrial valve designs.

    ORCID iDs

    Vasantha, Gokula, Purves, David ORCID logoORCID: https://orcid.org/0000-0001-8294-0706, Quigley, John, Corney, Jonathan ORCID logoORCID: https://orcid.org/0000-0003-1210-3827, Sherlock, Andrew and Randika, Geevin;