milling

This study presents a real-world IoT data-acquisition system connected to a machining center and evaluates various machine learning algorithms—particularly ensemble methods—for predicting machine states and motor temperatures under industrial conditions. The results show that ensemble techniques, especially Rotation Forests, outperform other methods in both classification and regression tasks, offering a robust and low-cost solution for extracting actionable insights from unbalanced and noisy manufacturing data.