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Conference paper

In-line 3D print failure detection using computer vision

In Dimensional Accuracy and Surface Finish in Additive Manufacturing — 2017
From

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
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

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