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The value of electronic waste at present is estimated to increase rapidly year after year, and with rapid advances in electronics, shows no signs of slowing down. Storage devices such as SATA Hard Disks and Solid State Devices are electronic devices with high value recyclable raw materials which often goes unrecovered. Most of the e-waste currently generated, including HDDs, is either managed by the informal recycling sector, or is improperly landfilled with the municipal solid waste, primarily due to insufficient recovery infrastructure and labor shortage in the recycling industry. This emphasizes the importance of developing modern advanced recycling technologies such as robotic disassembly. Performing smooth robotic disassembly operations of precision electronics necessitates fast and accurate geometric 3D profiling to provide a quick and precise location of key components. Fringe Projection Profilometry (FPP), as a variation of the well-known structured light technology, provides both the high speed and high accuracy needed to accomplish this. However, Using FPP for disassembly of high-precision electronics such as hard disks can be especially challenging, given that the hard disk platter is almost completely reflective. Furthermore, the metallic nature of its various components make it difficult to render an accurate 3D reconstruction. To address this challenge, We have developed a single-shot approach to predict the 3D point cloud of these devices using a combination of computer graphics, fringe projection, and deep learning. We calibrate a physical FPP-based 3D shape measurement system and set up its digital twin using computer graphics. We capture HDD and SSD CAD models at various orientations to generate virtual training datasets consisting of fringe images and their point cloud reconstructions. This is used to train the U-NET which is then found efficient to predict the depth of the parts to a high accuracy with only a single shot fringe image. This proposed technology has the potential to serve as a valuable fast 3D vision tool for robotic re-manufacturing and is a stepping stone for building a completely automated assembly system.
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Improving the Efficiency of Maxwell’s Equations FDTD Modeling for Space Weather Applications by Scaling the Speed of Light
Space weather can affect the Earth over time spans of hours and days. However, time-stepping increments for FDTD models are typically on the order of a fraction of a second. This paper introduces a means of increasing the time stepping increment’s upper limit by artificially slowing down the speed of light. Numerically slowing down the speed of light is achieved by appropriately modifying the permittivity, permeability, and conductivity values in the model. Proof-of-concept results are provided to show that the method works well for homogeneous media.
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- Award ID(s):
- 1662318
- PAR ID:
- 10182388
- Date Published:
- Journal Name:
- IEEE Antennas and Propagation Society International Symposium and North American Radio Science Meeting
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
