Mathematical morphology-based point cloud analysis techniques for geometry assessment of 3D printed concrete elements

Sooraj A.O. Nair, Gaurav Sant, Narayanan Neithalath

Research output: Contribution to journalArticlepeer-review

Abstract

In 3D printing of cement-based materials, it is imperative to ensure geometrical consistency of the print with the as-designed/modeled system. Time-dependent, deformable systems like concrete present multiple challenges in ensuring appropriate post-print quality. This paper presents a suite of point cloud comparison techniques, which can be used individually or in combination, to quantify the amount of mismatch between the as-designed and as-printed systems, using morphological analysis. A semi-quantitative error distance method is proposed, which can be easily accomplished using direct mapping of the actual and reference point clouds. A print accuracy index (PAI) based on centroidal distances is proposed as a global quantifier of the print quality. Furthermore, a topological set theory (TST)-based approach is used to determine layer-wise overlap, which helps in isolating localized inconsistencies. The methods are tested on a variety of small cuboids, and further verified using a larger mortar print. It is expected that these methodologies can be suitably adapted to indicate the efficiency of the print, after the fact, or during printing. The latter facilitates in-line quality checks, that can in turn lead to real-time alterations in the materials or processes.

Original languageEnglish (US)
Article number102499
JournalAdditive Manufacturing
DOIs
StateAccepted/In press - 2021
Externally publishedYes

Keywords

  • 3D printed concrete
  • Error distance
  • Mathematical morphology quality control
  • Point cloud analysis
  • Print accuracy index

ASJC Scopus subject areas

  • Biomedical Engineering
  • Materials Science(all)
  • Engineering (miscellaneous)
  • Industrial and Manufacturing Engineering

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