Prediction of milling performance of thermally modified wood based on machine learning

Abstract

The milling performance of thermally modified wood is an essential step in its actual processing and production. Accurate prediction of milling performance of thermally modified wood is significantly meaningful for subsequent parameter optimization to improve product surface quality and increase product competitiveness. Hence, based on machine learning, four models, Random Forest (RF), Support Vector Machine (SVM), Gaussian Process Regression (GPR) and Multilayer Perceptron (MLP), were established to predict two milling parameters of thermally modified wood. In addition, four characteristics factors were set up to evaluate the cutting force (F) and the surface roughness (Ra): the modification temperature (T) of thermally modified wood, the depth of cut (h), the feed rate (u), and the spindle speed (n) of the tool during the milling process. In order to reflect the scientific nature of the research process, normal distribution analysis was additionally used as a dataset preprocessing step. The final comparison found the GPR model to be the best fitting and most accurate method for predicting milling performance.