Positioning Fog Computing for Smart Manufacturing

• URL: http://arxiv.org/abs/2205.10860v1
• Date: Sun, 22 May 2022 16:03:29 GMT
• Title: Positioning Fog Computing for Smart Manufacturing
• Authors: Jaakko Harjuhahto and Vesa Hirvisalo
• Abstract summary: We study machine learning systems for real-time industrial quality control. We see machine learning as a viable choice for developing automated quality control systems. 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.
• Score: 1.0965065178451106
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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