Dogan, MD;
Acevedo Colon, SV;
Sinha, V;
Akşit, K;
Mueller, S;
(2021)
SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning.
In:
UIST '21: The 34th Annual ACM Symposium on User Interface Software and Technology.
(pp. pp. 24-38).
ACM
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Abstract
Laser cutter users face difficulties distinguishing between visually similar materials. This can lead to problems, such as using the wrong power/speed settings or accidentally cutting hazardous materials. To support users, we present SensiCut, an integrated material sensing platform for laser cutters. SensiCut enables material awareness beyond what users are able to see and reliably differentiates among similar-looking types. It achieves this by detecting materials' surface structures using speckle sensing and deep learning. SensiCut consists of a compact hardware add-on for laser cutters and a user interface that integrates material sensing into the laser cutting workflow. In addition to improving the traditional workflow and its safety1, SensiCut enables new applications, such as automatically partitioning designs when engraving on multi-material objects or adjusting their geometry based on the kerf of the identified material. We evaluate SensiCut's accuracy for different types of materials under different sheet orientations and illumination conditions.
| Type: | Proceedings paper |
|---|---|
| Title: | SensiCut: Material-Aware Laser Cutting Using Speckle Sensing and Deep Learning |
| Event: | The 34th Annual ACM Symposium on User Interface Software and Technology |
| ISBN-13: | 9781450386357 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3472749.3474733 |
| Publisher version: | https://doi.org/10.1145/3472749.3474733 |
| Language: | English |
| Additional information: | This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10138109 |
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