Inferring Quality In Point Cloud-Based 3D Printed Objects Using Topological Data Analysis

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 to compute a topological abstraction of the eventual printed model. This abstraction enables investigating certain qualities of the model, with respect to print quality, and identify potential anomalies that may appear in the final product.

Paul Rosen, Mustafa Hajij, Junyi Tu, Tanvirul Arafin, and Les Piegl. Inferring Quality In Point Cloud-Based 3D Printed Objects Using Topological Data Analysis. Computer-Aided Design and Applications, 2019.

@article{rosen2019inferring,
  title = {Inferring Quality in Point Cloud-based 3D Printed Objects using Topological
    Data Analysis},
  author = {Rosen, Paul and Hajij, Mustafa and Tu, Junyi and Arafin, Tanvirul and Piegl,
    Les},
  journal = {Computer-Aided Design and Applications},
  volume = {16},
  pages = {519--527},
  year = {2019},
  note = {textit{Presented at the CAD Conference and Exhibition 2019.}},
  abstract = {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 to compute a topological abstraction of the eventual
    printed model. This abstraction enables investigating certain qualities of the model,
    with respect to print quality, and identify potential anomalies that may appear in the
    final product. }
}