Conference paper
In-line 3D print failure detection using computer vision
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Visual Computing, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Department of Mechanical Engineering, Technical University of Denmark3
Manufacturing Engineering, Department of Mechanical Engineering, Technical University of Denmark4
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.
Language: | English |
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Year: | 2017 |
Proceedings: | euspen and ASPE Special Interest Group Meeting: Additive Manufacturing |
Types: | Conference paper |
ORCIDs: | Wilm, Jakob , Eiríksson, Eyþór Rúnar , Jensen, Janus Nørtoft , Aanæs, Henrik and Pedersen, David Bue |