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Collaborative Robot based Architecture to Train Flexible Automated Disassembly Systems for Critical Materials

机译:基于协作机器人的架构,用于训练临界材料的灵活自动拆卸系统

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The capability of disassembly operations contributes to controlling the volume and flow of materials in reverse feedback cycles. An inability to achieve certain levels of volume or flow in feedback cycles from end-of-use products can risk the efficiency and profitability of recycling, remanufacturing, and reuse. Disassembly systems face a significant challenge to achieving large-scale operations due to uncertainty in end-of-use product quality, quantity and timing. In order to manage these uncertainties efficiently, disassembly systems must be flexible, however, they must be automated to achieve large-scale operations. In this paper, a cyber-physical architecture is conceptualized utilizing human-robot interaction via collaborative robots (Cobot) to train a flexible automated disassembly system. As a first step to achieving this system, this paper proposes and demonstrates a method to extract critical disassembly information from a human-cobot disassembly training work-cell that would be used to distribute tasks to processes in a high volume disassembly line.
机译:拆卸操作的能力有助于控制反向反馈周期中的材料的体积和流量。无法实现使用末期产品的反馈循环中的一定程度的体积或流量可以危险回收,再制造和重用的效率和盈利能力。拆卸系统由于使用的使用产品质量,数量和时序而导致的大规模运营面临重大挑战。为了有效地管理这些不确定性,拆卸系统必须是灵活的,但必须自动化以实现大规模操作。在本文中,通过协作机器人(COBOT)利用人机机器人(COBOT)来训练灵活的自动拆卸系统的网络物理架构。作为实现该系统的第一步,本文提出并演示了一种从人Cobot拆卸训练工作单元中提取关键拆卸信息的方法,该方法将用于将任务分配给高容量拆卸线的过程。

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