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A Multiagent based Knowledge Extraction Framework to Support Plug and Produce Capabilities in Manufacturing Monitoring Systems

机译:基于多算法的知识提取框架,用于支持制造监控系统中的插头和生产能力

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摘要

The manufacturing industry has been steadily evolving over the years, with new market trends encouraging manufacturers to find new ways to meet the consumers' demands and quickly adapt to new business opportunities. Manufacturing systems are therefore required to be more and more agile and flexible in an environment dominated by unpredictable changes and disturbances. As a direct consequence several new solutions have been proposed, revolving around agility, flexibility, reconfigurability and modularity, enabling concepts such as Plug & Produce (P&P). Following this trend, the present article proposes a possible implementation for a multiagent-based knowledge extraction architecture to support P&P in flexible, distributed manufacturing monitoring systems. The validation process is also described, entailing the application of said system in a real industrial environment, more specifically monitoring two robotic cells performing the welding of a car's side member.
机译:多年来,制造业一直在发展,新的市场趋势鼓励制造商找到满足消费者需求并迅速适应新的商机的新方法。因此,在由不可预测的变化和干扰为主导的环境中,需要制造系统越来越敏捷,灵活。作为直接后果,已经提出了几种新解决方案,旋转了敏捷性,灵活性,重新配置性和模块化,使概念(如P&P)等概念为准。在此趋势之后,本文提出了一种基于多元技术的知识提取架构的实施,以支持灵活的分布式制造监控系统中的P&P。还描述了验证过程,意味着在真实的工业环境中应用所述系统,更具体地监测执行汽车侧构件焊接的两个机器人单元。

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