Prediction Models for the Milling of Heat-Treated Beech Wood Based on the Consumption of Energy

Abstract

This article is focused mainly on verifying the suitability of data from the experimental milling of heat-treated beech wood and on investigating the effects of the technical and technological parameters of milling on the energy consumption of this process. The independent parameters of the machining process are the cutting speed, feed speed, rake angle, and hydrothermal modification of the experimental wood material. Based on analysis of variance, it can be argued that the cutting speed and rake angle of the tool have the greatest statistically significant effect on energy consumption, while the feed speed has the least influence. The measured data on cutting power during milling were used to build a regression model and validate it, and the most suitable type of model, with a correlation of 87%, is the classification and regression tree, followed by a model created using the random forest method.

Keywords

• milling
• heat-treated wood
• energy consumption
• regression model
• C&RT model
• random forest