Machine Learning–Based Data Analysis in Python for Predicting Feed Speed in High-Productivity Sawmill Machines

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Abstract

The forestry industry has increasingly adopted Industry 4.0 technologies to improve productivity, efficiency, and decision-making in critical manufacturing processes. Among these processes, feed speed control in high-productivity sawmill machines plays a fundamental role, as it directly affects production performance, energy consumption, tool wear, and product quality. This study presents a machine learning–based data analysis approach implemented in Python to predict feed speed in industrial sawmilling operations using multivariate process data collected from production environments.The proposed methodology follows the CRISP-DM framework and integrates data preprocessing, dimensionality reduction, model training, and performance evaluation. A real-world dataset composed of more than 70 process variables was analyzed, including mechanical, operational, and sensor-based measurements. Principal Component Analysis (PCA) was applied to reduce data dimensionality while preserving the most relevant information. Several supervised machine learning models were implemented and compared, including Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Recurrent Neural Networks (RNN).Model performance was evaluated using regression metrics such as Mean Squared Error and the coefficient of determination (R²). The results demonstrate that all evaluated models achieved high predictive accuracy, with the recurrent neural network model showing the best overall performance, exceeding 93% correlation between predicted and actual feed speed values. These findings confirm the effectiveness of Python-based machine learning techniques for modeling complex industrial processes and provide a valuable decision-support tool for optimizing feed speed in high-productivity sawmill machines.

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