A comparison of three design tree based search algorithms for the detection of engineering parts constructed with CATIA V5 in large databases. Issue 3 (1st July 2014)

• Record Type: Journal Article
• Authors: Roj, Robin
• Is Part Of: Journal of computational design and engineering. Volume 1:Issue 3(2014)
• Page Start: 161
• Page End: 172
• Publication Date: 2014-07-01
• Subjects: CAD -- CATIA V5 -- Classification -- Database -- Dat mining -- Design tree -- Feature recognition -- Knowledge Discovery -- Python -- Search engine; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.7315/JCDE.2014.016
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: This paper presents three different search engines for the detection of CAD-parts in large databases. The analysis of the contained information is performed by the export of the data that is stored in the structure trees of the CAD-models. A preparation program generates one XML-file for every model, which in addition to including the data of the structure tree, also owns certain physical properties of each part. The first search engine is specializes in the discovery of standard parts, like screws or washers. The second program uses certain user input as search parameters, and therefore has the ability to perform personalized queries. The third one compares one given reference part with all parts in the database, and locates files that are identical, or similar to, the reference part. All approaches run automatically, and have the analysis of the structure tree in common. Files constructed with CATIA V5, and search engines written with Python have been used for the implementation. The paper also includes a short comparison of the advantages and disadvantages of each program, as well as a performance test.