Machine Learning for Paper Grammage Prediction Based on Sensor Measurements in Paper Mills
This work addresses a specific industrial automation challenge for paper mill operators, but it is incremental as it applies existing ML methods to sensor data.
The paper tackled the problem of automating paper grammage classification in paper mills to reduce human intervention and errors, achieving 97.1% accuracy, precision, and recall using the AdaBoost algorithm.
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.