论文标题
定位智能制造的雾计算
Positioning Fog Computing for Smart Manufacturing
论文作者
论文摘要
我们研究实时工业质量控制的机器学习系统。在许多工厂系统中，必须连续控制生产过程以保持产品质量。特别具有挑战性的是必须在严格的资源消耗限制和有缺陷的最终产品的风险之间实时平衡的系统。由于人类控制繁琐且容易出错，因此需要自动质量控制系统。我们认为机器学习是开发自动化质量控制系统的可行选择，但是将这种系统与现有的工厂自动化集成在一起仍然是一个挑战。在本文中，我们建议将新的雾计算层引入自动化控制的标准层次结构，以满足机器学习驱动质量控制的需求。
We study machine learning systems for real-time industrial quality control. In many factory systems, production processes must be continuously controlled in order to maintain product quality. Especially challenging are the systems that must balance in real-time between stringent resource consumption constraints and the risk of defective end-product. There is a need for automated quality control systems as human control is tedious and error-prone. We see machine learning as a viable choice for developing automated quality control systems, but integrating such system with existing factory automation remains a challenge. In this paper we propose introducing a new fog computing layer to the standard hierarchy of automation control to meet the needs of machine learning driven quality control.
