Common design structures and substitutable feature discovery in CAD databases

Gokula Vasantha,David Purves,John Quigley,Jonathan Corney,Andrew Sherlock,Geevin Randika

doi:10.1016/j.aei.2021.101261

Gokula Vasantha, David Purves + Show 4 more

Open Access

https://doi.org/10.1016/j.aei.2021.101261

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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.

Highlights

Many large corporations have product portfolios that span multiple generations and physical sites
The reuse of engineering components has established metrics and methodologies, the reuse of specific design features has received less attention in the literature. The nature of such design features varies from product to product but can be defined generically as a “portion of the product geometry that is of design significance”
This research work aims to assess the potential of Association Rule Mining to support: common design structure (CDS) extraction in CAD models and find ‘substitutable features’ within emerging CDSs

Summary

Introduction

Many large corporations have product portfolios that span multiple generations and physical sites. 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. The nature of such design features varies from product to product but can be defined generically as a “portion of the product geometry that is of design significance”. Instead of searching CAD models to identify instances of particular patterns of local geometry (i.e. geometric feature recogni tion), the aim is to find shared patterns in the feature content of multiple models. We are seeking to recognise sets of features that frequently occur together in a CAD database

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