Similarity of Tessellated Solid Models for Engineering Applications

Autor: Rahul Sharan Renu, Christopher Sousa
Rok vydání: 2018
Předmět:
Zdroj: Volume 1B: 38th Computers and Information in Engineering Conference.
DOI: 10.1115/detc2018-85269
Popis: The objective of this research is to investigate the performance of a solid model similarity assessment method. This method is used to assess the similarity of tessellated solid models, where the tessellated geometry is in the form of triangles — specifically, the method compares STL files. A histogram of (triangle) tessellation areas is generated for each solid model being compared. The difference in the histograms of two solid models indicates their dissimilarity. The performance of the solid model similarity assessment method is evaluated by varying tessellation resolutions, and by varying histogram bin sizes. The solid model similarity assessment method is also compared to methods from literature. The comprehensive testing was performed using 867 solid models from the Engineering Shape Benchmark. It is found that the method was robust in its sensitivity to histogram bin sizes, and robust in its sensitivity to tessellation resolution. It is found that for small retrieval sizes, precision is relatively high. It is also found that this method outperformed methods from literature when comparing models that are rectangular, flat, thin, and/or cubic. Additionally, shortcomings of this method and related future work is identified.
Databáze: OpenAIRE