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In recent years, the concentration of precious metals and hazardous pollutants in discarded consumer-grade computer enclosures has increased significantly, coinciding with e-waste generation in Asia reaching approximately 30 million tons annually. However, the high cost and low efficiency of manual disassembly present substantial obstacles to the effective recycling of such enclosures. Robotic disassembly has emerged as a promising alternative. To enable accurate acquisition of three-dimensional (3D) geometric data for robotic operations, we propose a 3D measurement method based on multi-color high dynamic range imaging. This method employs a seven-color illumination strategy and exploits the spectral response characteristics of a color camera to different wavelengths, effectively mitigating the reconstruction errors caused by overexposure on highly reflective surfaces—an issue common in traditional techniques. The proposed approach provides complete and reliable 3D morphological information to support robotic arm manipulation. Experimental results confirm that the method accurately captures the 3D profiles of reflective components such as CPUs and motherboards. Moreover, validation across computer enclosures of different brands and form factors demonstrates the method’s robustness and practical applicability in a wide range of e-waste disassembly scenarios. 

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.