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.Abstract
Bakelite Chemicals LLC has developed an innovative microscopy technique to view adhesives “in use.” The cornerstone of the method is based on sample preparation. Samples of a composite wood product are sanded to a highly polished surface using a progression of sandpaper grits from lowest to highest. This preparation technique differs significantly from historical preparation techniques, which rely on soaking or embedding samples followed by thin-section creation using a microtome. The wet preparation technique causes wood cells compressed during production of the composite to swell and change how the adhesive is seen within those cells. The addition of water to the sample can also move uncured adhesive around the bond line. These changes can significantly alter the interpretation of how the adhesive performs during the manufacturing process. The new preparation technique combined with standard fluorescence microscopy has provided a groundbreaking and accessible ability to understand how an adhesive responds to process changes in manufacturing. Improving the understanding of the dynamic relationship between the adhesive and manufacturing processes will be crucial in helping the wood products industry take advantage of the rapid improvements in processing technology.Abstract