Year: 2022
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Harjuhahto, Jaakko, Hirvisalo, Vesa
Institution: Aalto University
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4281483355