A Machine Learning Approach to Predict Properties of Wood Products during Milling

Abstract

Abstract The wood industry seeks innovative methods to improve process monitoring and adaptive control by modeling workpiece characteristics. This study proposes a sensor fusion approach that integrates data from airborne sound, cutting forces, power consumption, and acoustic emissions while milling diverse wood-based products. The objective of this research is to accurately predict workpiece attributes, such as the density of the wood products to achieve strength grading and the roughness of the machined surfaces to identify tool wear or unsuitable process parameters. To accomplish this objective, machine learning regression was employed by training a model on the predictors chosen through supervised univariate feature ranking. Individual linear regression models per workpiece type depended heavily on the material, where the validation R2 values ranged from 0.1 to 0.99, due to presplitting in the case of samples machined across the fiber and porosity in the case of particleboard samples. A validation R2 of 0.99 could be achieved for the collective modeling of density based on all the collected samples, with samples machined against the fiber being excluded. Surface roughness could be predicted with a validation R2 of 0.91 by excluding samples machined across the fiber and particleboards.