Inferring quality in point cloud-based 3D printed objects using topological data analysis

被引：0
|
作者：
Rosen P. [1 ]
Hajij M. [1 ]
Tu J. [1 ]
Arafin T. [1 ]
Piegl L. [1 ]
机构：
[1] University of South Florida, United States
来源：
Computer-Aided Design and Applications | 2019年 / 16卷 / 03期
基金：
美国国家科学基金会;
关键词：
3D printing; Point cloud; Topological data analysis;
D O I：
10.14733/cadaps.2019.519-527
中图分类号：
学科分类号：
摘要：
Assessing the quality of 3D printed models before they are printed remains a challenging problem, particularly when considering point cloud-based models. This paper introduces an approach to quality assessment, which uses techniques from the field of Topological Data Analysis (TDA) to compute a topological abstraction of the eventual printed model. Two main tools of TDA, Mapper and persistent homology, are used to analyze both the printed space and empty space created by the model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identifies potential anomalies that may appear in the final product. © 2019 CAD Solutions, LLC.
引用
收藏
页码：519 / 527
页数：8
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