Positioning Fog Computing for Smart Manufacturing

TL;DR

This paper proposes integrating a fog computing layer into factory automation hierarchies to enhance real-time machine learning-based quality control, balancing resource constraints and defect risk in smart manufacturing.

Contribution

It introduces a novel fog computing architecture specifically designed for real-time industrial quality control with machine learning integration.

Findings

Enhanced real-time processing capabilities

Improved resource management in manufacturing

Potential reduction in defective products

Abstract

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.