Version 1
: Received: 3 December 2019 / Approved: 4 December 2019 / Online: 4 December 2019 (12:26:15 CET)
How to cite:
Abbas, H. Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills. Preprints2019, 2019120051. https://doi.org/10.20944/preprints201912.0051.v1
Abbas, H. Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills. Preprints 2019, 2019120051. https://doi.org/10.20944/preprints201912.0051.v1
Abbas, H. Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills. Preprints2019, 2019120051. https://doi.org/10.20944/preprints201912.0051.v1
APA Style
Abbas, H. (2019). Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills. Preprints. https://doi.org/10.20944/preprints201912.0051.v1
Chicago/Turabian Style
Abbas, H. 2019 "Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills" Preprints. https://doi.org/10.20944/preprints201912.0051.v1
Abstract
Automation is at the core of modern industry. It aims to increase production rates, decrease production costs, and reduce human intervention in order to avoid human mistakes and time delays during manufacturing. On the other hand, human assistance is usually required to customize products and reconfigure control systems through a special process interface called Human Machine Interface (HMI). Machine Learning (ML) algorithms can effectively be used to resolve this tradeoff between full automation and human assistance. This paper provides an example of the industrial application of ML algorithms to help human operators save their mental effort and avoid time delays and unintended mistakes for the sake of high production rates. Based on real-time sensor measurements, several ML algorithms have been tried to classify paper rolls according to paper grammage in a white paper mill. The performance evaluation shows that the AdaBoost algorithm is the best ML algorithm for this application with classification accuracy (CA), precision, and recall of 97.1%. The generalization of the proposed approach for achieving a cost-effective mill construction by reducing the total number of the required physical sensors will be the subject of our future research.
industrial informatics; automation; machine learning; paper grammage classification; paper mills
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
