Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Rapidly finding CAD features using database optimization

Niu, Zhibin, Martin, Ralph Robert, Langbein, Frank Curd and Sabin, Malcolm A. 2015. Rapidly finding CAD features using database optimization. Computer-Aided Design 69 , pp. 35-50. 10.1016/j.cad.2015.08.001

[img]
Preview
PDF - Accepted Post-Print Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Automatic feature recognition aids downstream processes such as engineering analysis and manufacturing planning. Not all features can be defined in advance; a declarative approach allows engineers to specify new features without having to design algorithms to find them. Naive translation of declarations leads to executable algorithms with high time complexity. Database queries are also expressed declaratively; there is a large literature on optimizing query plans for efficient execution of database queries. Our earlier work investigated applying such technology to feature recognition, using a testbed interfacing a database system (SQLite) to a CAD modeler (CADfix). Feature declarations were translated into SQL queries which are then executed. The current paper extends this approach, using the PostgreSQL database, and provides several new insights: (i) query optimization works quite differently in these two databases, (ii) with care, an approach to query translation can be devised that works well for both databases, and (iii) when finding various simple common features, linear time performance can be achieved with respect to model size, with acceptable times for real industrial models. Further results also show how (i) lazy evaluation can be used to reduce the work performed by the CAD modeler, and (ii) estimating the time taken to compute various geometric operations can further improve the query plan. Experimental results are presented to validate our main conclusions.

Item Type: Article
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Uncontrolled Keywords: Feature recognition; Database query planning; Declarative features
Publisher: Elsevier
ISSN: 0010-4485
Funders: European Research Council
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 23 July 2015
Last Modified: 30 Jun 2019 22:50
URI: http://orca.cf.ac.uk/id/eprint/83776

Citation Data

Cited 14 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics