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.

翻译：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,为了保持产品质量,生产过程必须不断受到控制。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风险之间实现实时平衡的系统。需要自动质量控制系统,因为人体控制是乏味和容易出错的。我们认为机器学习是发展自动化质量控制系统的可行选择,但将这种系统与现有的工厂自动化结合起来仍是一项挑战。在本文件中,我们提议在自动化控制的标准等级中引入一个新的雾计算层,以满足机器学习驱动质量控制的需要。