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Al tools for use in assembly automation and some examples of recent applications

机译：用于装配自动化的Al工具以及最近应用的一些示例

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Purpose - This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge-based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case-based reasoning and ambient-intelligence. Design/methodology/approach - Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation. Findings - Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present-day computers. Research limitations/implications - Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low-capability microcontrollers. Practical implications - It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace... but it is expanding. The appropriate deployment of the new Al tools will contribute to the creation of more competitive assembly automation systems. Social implications - Other technological developments in Al that will impact on assembly automation include data mining, multi-agent systems and distributed self-organising systems. Originality/value - The novel approaches proposed use ambient intelligence and the mixing of different Al tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing. Flexible assembly, Small batch production, Industrial robotics, Vision, Flexible assembly systems,
机译：目的-本文旨在回顾可用于装配自动化的七个人工智能工具：基于知识的系统，模糊逻辑，自动知识获取，神经网络，遗传算法，基于案例的推理和环境智能。设计/方法/方法-概述了每个人工智能工具，以及它们在装配自动化中使用的一些示例。调查结果-人工智能产生了许多有用而强大的工具。本文回顾了其中一些工具。由于当今计算机的强大功能和价格可承受性，这些工具在装配自动化中的应用已变得更加广泛。研究的局限性/含义-可能会出现许多新的装配自动化应用程序，并且可能会使用混合工具，这些工具结合了本文中介绍的两个或多个工具的优势。本文中介绍的工具和方法具有最小的计算复杂度，可以在小型装配线，单个机器人或具有低能力微控制器的系统上实现。实际意义-考虑到当前缺乏熟悉度以及与使用这些工具相关的技术障碍，工程师可能需要再花十年的时间才能意识到好处，而且直接数字制造被认为很平常可能要花很长时间...但是扩展。新Al工具的适当部署将有助于创建更具竞争力的装配自动化系统。社会影响-铝的其他会影响装配自动化的技术发展包括数据挖掘，多代理系统和分布式自组织系统。独创性/价值-提出的新颖方法利用环境智能和各种Al工具的混合，以尽力利用每种技术的优势。这些概念通常适用于所有工业装配过程，本研究旨在证明这些概念可在制造中使用。柔性组装，小批量生产，工业机器人，视觉，柔性组装系统，

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