Implementation of persistent identification of topological entities based on macro-parametrics approach. Issue 2 (19th January 2016)

• Record Type: Journal Article
• Authors: Farjana, Shahjadi Hisan; Han, Soonhung; Mun, Duhwan
• Is Part Of: Journal of computational design and engineering. Volume 3:Issue 2(2016)
• Page Start: 161
• Page End: 177
• Publication Date: 2016-01-19
• Subjects: Feature based CAD -- Macro-parametrics approach -- Persistent identification -- TransCAD -- Topological element; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1016/j.jcde.2016.01.001
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: In history based parametric CAD modeling systems, persistent identification of the topological entities after design modification is mandatory to keep the design intent by recording model creation history and modification history. Persistent identification of geometric and topological entities is necessary in the product design phase as well as in the re-evaluation stage. For the identification, entities should be named first according to the methodology which will be applicable for all the entities unconditionally. After successive feature operations on a part body, topology based persistent identification mechanism generates ambiguity problem that usually stems from topology splitting and topology merging. Solving the ambiguity problem needs a complex method which is a combination of topology and geometry. Topology is used to assign the basic name to the entities. And geometry is used for the ambiguity solving between the entities. In the macro parametrics approach of iCAD lab of KAIST a topology based persistent identification mechanism is applied which will solve the ambiguity problem arising from topology splitting and also in case of topology merging. Here, a method is proposed where no geometry comparison is necessary for topology merging. The present research is focused on the enhancement of the persistent identification schema for the support of ambiguity problem especially of topology splitting problem and topology merging problem. It also focused on basic naming of pattern features.