Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills

TL;DR

This paper demonstrates how machine learning algorithms, especially AdaBoost, can accurately classify paper grammage in real-time using sensor data, enhancing automation and reducing human effort in paper mills.

Contribution

The study applies multiple ML algorithms to classify paper grammage from sensor data, identifying AdaBoost as the most effective with 97.1% accuracy, advancing industrial automation.

Findings

AdaBoost achieved 97.1% classification accuracy.

Sensor-based ML classification can assist human operators.

Potential for cost-effective mill construction.

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 cost-effective mills construction will be the subject of our future research.