Graph Learning for Cognitive Digital Twins in Manufacturing Systems
Graph Learning for Cognitive Digital Twins in Manufacturing Systems
复制标题
制造系统中认知数字孪生的图学习
DOI：
10.1109/tetc.2021.3132251
复制
发表时间：
2021-09
影响因子：
5.9
通讯作者：
Trier Mortlock;Deepan Muthirayan;S. Yu;P. Khargonekar;Mohammad Abdullah Al Faruque
中科院分区：
文献类型：
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作者：
Trier Mortlock;Deepan Muthirayan;S. Yu;P. Khargonekar;Mohammad Abdullah Al Faruque
未来的制造业需要复杂的系统，将仿真平台和虚拟化与来自工业流程的物理数据连接起来。数字双胞胎包括物理双胞胎，数字双胞胎以及两者之间的联系。使用数字双胞胎的好处非常多，特别是在制造业中，因为它们可以提高整个制造生命周期的效率。随着时间的推移，数字孪生概念变得越来越复杂和强大，这是由于许多技术的兴起。在本文中，我们详细介绍了认知数字孪生模型，作为数字孪生模型的下一个发展阶段，这将有助于实现工业4.0的愿景。认知数字孪生将使企业能够创造性地、有效地、高效地利用从现有制造系统的经验中获得的隐性知识。它们还可以实现更多的自主决策和控制，同时提高整个企业的性能（大规模）。本文提出了图形学习作为一种潜在的途径，以实现制造数字双胞胎的认知功能。提出了一种利用图学习在制造业产品设计阶段实现认知数字孪生的新方法。
Future manufacturing requires complex systems that connect simulation platforms and virtualization with physical data from industrial processes. Digital twins incorporate a physical twin, a digital twin, and the connection between the two. Benefits of using digital twins, especially in manufacturing, are abundant as they can increase efficiency across an entire manufacturing life-cycle. The digital twin concept has become increasingly sophisticated and capable over time, enabled by rises in many technologies. In this article, we detail the cognitive digital twin as the next stage of advancement of a digital twin that will help realize the vision of Industry 4.0. Cognitive digital twins will allow enterprises to creatively, effectively, and efficiently exploit implicit knowledge drawn from the experience of existing manufacturing systems. They also enable more autonomous decisions and control, while improving the performance across the enterprise (at scale). This article presents graph learning as one potential pathway towards enabling cognitive functionalities in manufacturing digital twins. A novel approach to realize cognitive digital twins in the product design stage of manufacturing that utilizes graph learning is presented.