Computer Vision for Video-Based Material Strain Extraction
ORAL
Abstract
To advance the search for 0νββ decay, the LEGEND-1000 experiment will require scaling-up from its predecessor, LEGEND-200; the cryostat in particular will contain a copper reentrant tube (RT) in order to create separate volumes of underground and atmospheric argon. As the thinnest part of the RT will only be ~3 mm thick, small-scale pressure and strain testing is underway to confirm structural simulations. During the course of these strain tests, video recordings of the testing process were taken and we opted to use machine learning (ML) techniques to perform a frame-by-frame analysis of the footage, extracting the outline of the test cylinders and thereby tracking material deformation with time and pressure.
Meta’s computer vision software Segment Anything Model 2 (SAM2) was applied to video footage of two of the pressure tests; RT diameters were extracted at multiple z-levels from the masks produced by SAM2. Optical character recognition software was used to extract timestamps from the videos, allowing for the synchronization of RT deformation and pressure. Following a physics-based analysis, the yield strength of the copper was found to be consistent with expectations given the material and tube geometry. This demonstrates the effectiveness of non-contact ML-based strain analysis for experiment validation and opens the door for future applications in instrumentation.
Meta’s computer vision software Segment Anything Model 2 (SAM2) was applied to video footage of two of the pressure tests; RT diameters were extracted at multiple z-levels from the masks produced by SAM2. Optical character recognition software was used to extract timestamps from the videos, allowing for the synchronization of RT deformation and pressure. Following a physics-based analysis, the yield strength of the copper was found to be consistent with expectations given the material and tube geometry. This demonstrates the effectiveness of non-contact ML-based strain analysis for experiment validation and opens the door for future applications in instrumentation.
*This work is supported by the U.S. DOE, and the NSF, the LANL, ORNL and LBNL LDRD programs; the European ERC and Horizon programs; the German DFG, BMBF, and MPG; the Italian INFN; the Polish NCN and MNiSW; the Czech MEYS; the Slovak RDA; the Swiss SNF; the UK STFC; the Canadian NSERC and CFI; the LNGS and SURF facilities.
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Publication: We plan to prepare a manuscript for the AI methodology described in this talk.
Presenters
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Sonata B Simonaitis-Boyd
- University of California, San Diego