In-line 3D print failure detection using computer vision

Rasmus Ahrenkiel Lyngby, Jakob Wilm, Eyþór Rúnar Eiríksson, Jannik Boll Nielsen, Janus Nørtoft Jensen, Henrik Aanæs, David Bue Pedersen

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Abstract

Here we present our findings on a novel real-time vision system that allows for automatic detection of failure conditions that are considered outside of nominal operation. These failure modes include warping, build plate delamination and extrusion failure. Our system consists of a calibrated camera whose position and orientation is known in the machine coordinate system. We simulate what the object under print should look like for any given moment in time. This is compared to a segmentation of the current print, and statistical detection of significant deviation. We demonstrate that this methodology precisely and unambiguously detects the time point of print failure.
Original languageEnglish
Title of host publicationDimensional Accuracy and Surface Finish in Additive Manufacturing
Number of pages4
Publication date2017
Publication statusPublished - 2017
Eventeuspen and ASPE Special Interest Group Meeting: Additive Manufacturing: Dimensional Accuracy and Surface Finish from Additive Manufacturing - Katholieke Universiteit Leuven, Leuven, Belgium
Duration: 10 Oct 201711 Oct 2017

Conference

Conferenceeuspen and ASPE Special Interest Group Meeting: Additive Manufacturing
LocationKatholieke Universiteit Leuven
CountryBelgium
CityLeuven
Period10/10/201711/10/2017

Keywords

  • Failure detection
  • Computer vision
  • Fused deposition modeling (FDM)

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