Developing an IoT and machine learning-based monitoring system for discrete production processes

Abstract

PURPOSE: This paper aims to develop a tool to support discrete manufacturing process monitoring using IoT sensors and machine learning systems.

DESIGN/METHODOLOGY/APPROACH: Machine learning was used to prepare and analyze data from the production line. In discrete manufacturing, measurements from sensors throughout the line at various locations are read for objects moving on the line. The measurements and related research allow for ongoing data analysis and earlier reactions to multiple critical situations.

FINDINGS: The study's result was the measurement data analysis in a discrete manufacturing process. Data was obtained from continuous monitoring of technological processes. It also shows how to classify components on the production line, allowing for better decision-making under uncertainty.

PRACTICAL IMPLICATIONS: The presented method of preparation and analysis of measurement data will allow for better production management and observation of the quality of this production.

ORIGINALITY/VALUE: A novelty is using an approach to data preparation and processing, neural network systems preparation, and element classification on the production line.