Positioning Fog Computing for Smart Manufacturing
This addresses the problem of automated quality control in smart manufacturing, but it appears incremental as it builds on existing fog computing concepts.
The paper tackles the challenge of integrating machine learning for real-time industrial quality control by proposing a new fog computing layer in automation hierarchies, aiming to balance resource constraints and defect risks.
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.