  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.
