Suction Cup Quality Predication by Digital Image Correlation

Event
WCX SAE World Congress Experience
Authors Abstract
Content
Vacuum suction cups are used as transforming handles in stamping lines, which are essential in developing automation and mechanization. However, the vacuum suction cup will crack due to fatigue or long-term operation or installation angle, which directly affects production productivity and safety. The better design will help increase the cups' service life. If the location of stress concentration can be predicted, this can prevent the occurrence of cracks in advance and effectively increase the service life. However, the traditional strain measurement technology cannot meet the requirements of tracking large-field stains and precise point tracking simultaneously in the same area, especially for stacking or narrow parts of the suction cups. The application must allow multiple measurements of hidden component strain information in different fields of view, which would add cost. In this study, a unique multi-camera three-dimensional digital image correlation (3D-DIC) system was designed and applied to measure the strain concentration of the suction cups while the cups were running the pulling progress. In this technique, a multiplexed quad-cameras DIC system which contains two sets of 3D-DIC system (4 cameras) with different field of view or different measurement directions enables simultaneous measurement of full-filed and hidden parts under the same calibration progress. The first two cameras built a sub-group of the 3D-DIC system, which was used to measure the local strain of the narrow or stacked prats. The other system was used to acquire the strain fields of the entire suction cup. In addition to the experimental test, the fatigue test to see the cracks appeared location. The results of DIC were compared to the fatigue data, and the DIC experimental data validated the crack location. This project aims to help designers and operators thoroughly understand the performance of vacuum cups by investigating the strain concentration and crack location.
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DOI
https://doi.org/10.4271/2023-01-0067
Pages
9
Citation
Guo, B., Zheng, X., Fang, S., and Yang, L., "Suction Cup Quality Predication by Digital Image Correlation," Advances and Current Practices in Mobility 5(6):2047-2055, 2023, https://doi.org/10.4271/2023-01-0067.
Additional Details
Publisher
Published
Apr 11, 2023
Product Code
2023-01-0067
Content Type
Journal Article
Language
English