Abstract
| Original language | English |
|---|---|
| Title of host publication | Dimensional Accuracy and Surface Finish in Additive Manufacturing |
| Number of pages | 4 |
| Publication date | 2017 |
| Publication status | Published - 2017 |
| Event | euspen and ASPE Special Interest Group Meeting: Additive Manufacturing: Dimensional Accuracy and Surface Finish from Additive Manufacturing - Katholieke Universiteit Leuven, Leuven, Belgium Duration: 10 Oct 2017 → 11 Oct 2017 |
Conference
| Conference | euspen and ASPE Special Interest Group Meeting: Additive Manufacturing |
|---|---|
| Location | Katholieke Universiteit Leuven |
| Country | Belgium |
| City | Leuven |
| Period | 10/10/2017 → 11/10/2017 |
Keywords
- Failure detection
- Computer vision
- Fused deposition modeling (FDM)
Cite this
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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 proceeding › Article in proceedings › Research › peer-review
TY - GEN
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
Y1 - 2017
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
ER -