Presentation + Paper
14 November 2021 A measurement method of curvature radius and shell thickness of laser fusion capsule by combining computer vision and virtual laser differential confocal technology
Author Affiliations +
Abstract
We propose a measurement method of curvature radius and shell thickness of laser fusion capsule by combining computer vision and virtual laser differential confocal technology. We use a CCD to photograph the surface of the capsule, to obtain the subpixel coordinates of the contour edge of the capsule. The qualified capsule can be quickly selected by using the least square fitting algorithm to fit the outer curvature radius. We then use the virtual laser differential confocal detection device to measure the position of the vertex on the outer surface of the capsule, the vertex on the inner surface of the capsule and the center of the capsule. Based on the characteristic that the zero-crossing of the laser differential confocal curve accurately corresponds to the focus of the objective lens, the outer and inner curvature radius and shell thickness of the capsule are measured precisely. The experimental results show that the repeatability of measurement of the outer curvature radius, inner curvature radius and shell thickness are 23 nm, 41 nm and 41 nm, respectively. Compared with the existing measurement methods, the capsule whose curvature radius and shell thickness are qualified can be accurately and quickly selected from the batch capsule using the method, which provides a technical support to the quality characterization of the laser fusion capsule.
Conference Presentation
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Xianxian Ma, Lirong Qiu, and Yun Wang "A measurement method of curvature radius and shell thickness of laser fusion capsule by combining computer vision and virtual laser differential confocal technology", Proc. SPIE 11914, SPIE Future Sensing Technologies 2021, 119140V (14 November 2021); https://doi.org/10.1117/12.2615583
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KEYWORDS
Laser applications

Confocal microscopy

Laser welding

Computer vision technology

Machine vision

Photography

Sensors

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