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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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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)

Cite this

Lyngby, R. A., Wilm, J., Eiríksson, E. R., Nielsen, J. B., Jensen, J. N., Aanæs, H., & Pedersen, D. B. (2017). In-line 3D print failure detection using computer vision. In Dimensional Accuracy and Surface Finish in Additive Manufacturing
Lyngby, Rasmus Ahrenkiel ; Wilm, Jakob ; Eiríksson, Eyþór Rúnar ; Nielsen, Jannik Boll ; Jensen, Janus Nørtoft ; Aanæs, Henrik ; Pedersen, David Bue. / In-line 3D print failure detection using computer vision. Dimensional Accuracy and Surface Finish in Additive Manufacturing. 2017.
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title = "In-line 3D print failure detection using computer vision",
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.",
keywords = "Failure detection, Computer vision, Fused deposition modeling (FDM)",
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Lyngby, RA, Wilm, J, Eiríksson, ER, Nielsen, JB, Jensen, JN, Aanæs, H & Pedersen, DB 2017, In-line 3D print failure detection using computer vision. in Dimensional Accuracy and Surface Finish in Additive Manufacturing. euspen and ASPE Special Interest Group Meeting: Additive Manufacturing, Leuven, Belgium, 10/10/2017.

In-line 3D print failure detection using computer vision. / Lyngby, Rasmus Ahrenkiel; Wilm, Jakob; Eiríksson, Eyþór Rúnar; Nielsen, Jannik Boll; Jensen, Janus Nørtoft; Aanæs, Henrik; Pedersen, David Bue.

Dimensional Accuracy and Surface Finish in Additive Manufacturing. 2017.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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T1 - In-line 3D print failure detection using computer vision

AU - Lyngby, Rasmus Ahrenkiel

AU - Wilm, Jakob

AU - Eiríksson, Eyþór Rúnar

AU - Nielsen, Jannik Boll

AU - Jensen, Janus Nørtoft

AU - Aanæs, Henrik

AU - Pedersen, David Bue

PY - 2017

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N2 - 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.

AB - 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.

KW - Failure detection

KW - Computer vision

KW - Fused deposition modeling (FDM)

M3 - Article in proceedings

BT - Dimensional Accuracy and Surface Finish in Additive Manufacturing

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Lyngby RA, Wilm J, Eiríksson ER, Nielsen JB, Jensen JN, Aanæs H et al. In-line 3D print failure detection using computer vision. In Dimensional Accuracy and Surface Finish in Additive Manufacturing. 2017