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Performance Study of an Innovative Collaborative Robot Gripper Design on Different Fabric Pick and Place Scenarios
Technical Paper
2020-01-1304
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Light-weighting fiber composite materials introduced to reduce vehicle mass and known as innovative materials research activities since they provide high specific stiffness and strength compared to contemporary engineering materials. Nonetheless, there are issues related automation strategies and handling methods. Material handling of flexible textile/fiber components is a process bottleneck and it is currently being performed by setting up multi-stage manual operations for hand layups. Consequently, the long-term research objective is to develop semi-automated pick and place processes for flexible materials utilizing collaborative robots within the process. The immediate research is to experimentally validate innovatively designed grippers for efficient material pick and place tasks. Pick and place experiments on a 0/90 plain woven carbon fiber fabric with an innovative gripper design is tested using a YuMi 14000 ABB collaborative robot to validate the new-designed gripper enhanced performance on the slippage and material wrinkling based on the previous research [20] for two gripping forces, and two travel speeds. Also, different double arm pick and place scenarios are sought to achieve an acceptable approach through which fabric wrinkling and placement accuracy are improved. It is shown that using modified 2nd generation silicone gloves, material slippage is completely prevented. In addition, it is figured out that double arm pick and place scenarios performed on fabrics pre-folded 2cm in each side, represents the enhanced wrinkling and placing accuracy over all other scenarios examined.
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Alebooyeh, M., Wang, B., and Urbanic, R., "Performance Study of an Innovative Collaborative Robot Gripper Design on Different Fabric Pick and Place Scenarios," SAE Technical Paper 2020-01-1304, 2020, https://doi.org/10.4271/2020-01-1304.Data Sets - Support Documents
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