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In this contribution, we present a novel 3D printed multi-material, electromagnetic vibration harvester. The harvester is based on a cantilever design and utilizes an embedded constantan wire within a matrix of polyethylene terephthalate glycol (PETG). A prototype has been manufactured with a combination of a fused filament fabrication (FFF) printer and a robot with a custom-made tool.
This paper introduces the use of convolutional neural networks to detect and classify humans and robots in Human-Robot Collaboration workspaces based on their thermal radiation power. The measurement setup includes an infrared camera, two cobots and up to two persons walking or interacting with the cobots in industrial settings. The chosen architectures are the YOLOv5 and YOLOv8 in different model sizes. The results are promising, showing real-time object detection in industrial settings with up to 303 fps with the YOLOv8n model. YOLOv5m achieves the best mAP50 result at 99.2% and the YOLOv5m achieves the best mAP50-95 at 85.8%
The objective of this work is to automate thread cutting of internal threads using an industrial robot. The system is designed to accommodate various sheet sizes based on the robot's working range. The robot controller records the data of axis 6 during the cutting process, which will be applied for process and quality analysis. This axis was utilised for the cutting process. An optical displacement sensor determines the accuracy of the moving axis. The robot's endlessly rotating axis 6 offers further advantages, including no additional drive for cutting, maximum flexibility, high automation, and a large working range of up to 800 ∙ 800 mm depending on the robot. The maximum torque derived from the cutting process was 3.52 Nm. The radial movement of the chuck was measured at ∆r = 0.27 mm, which was 60 times higher than the planned data.