Cognitive manufacturing: definition and current trends

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Fadi El Kalach, Ibrahim Yousif, Thorsten Wuest, Amit Sheth, Ramy Harik
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引用次数: 0

Abstract

Manufacturing systems have recently witnessed a shift from the widely adopted automated systems seen throughout industry. The evolution of Industry 4.0 or Smart Manufacturing has led to the introduction of more autonomous systems focused on fault tolerant and customized production. These systems are required to utilize multimodal data such as machine status, sensory data, and domain knowledge for complex decision making processes. This level of intelligence can allow manufacturing systems to keep up with the ever-changing markets and intricate supply chain. Current manufacturing lines lack these capabilities and fall short of utilizing all generated data. This paper delves into the literature aiming at achieving this level of complexity. Firstly, it introduces cognitive manufacturing as a distinct research domain and proposes a definition by drawing upon various preexisting themes. Secondly, it outlines the capabilities brought forth by cognitive manufacturing, accompanied by an exploration of the associated trends and technologies. This contributes to establishing the foundation for future research in this promising field.

Abstract Image

认知型制造:定义和当前趋势
最近,制造系统发生了转变,不再是工业领域广泛采用的自动化系统。工业 4.0 或智能制造的发展导致引入了更多注重容错和定制生产的自主系统。这些系统需要利用多模态数据(如机器状态、感官数据和领域知识)进行复杂的决策过程。这种智能水平可使制造系统跟上瞬息万变的市场和错综复杂的供应链。目前的生产线缺乏这些能力,无法利用所有生成的数据。本文深入研究了旨在实现这种复杂程度的文献。首先,本文介绍了认知型制造这一独特的研究领域,并借鉴各种已有主题提出了定义。其次,本文概述了认知制造所带来的能力,并对相关趋势和技术进行了探讨。这有助于为这一前景广阔的领域的未来研究奠定基础。
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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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