A decision tree-assisted polynomial regression model with application in the cutting force analysis of cutters of a tunnel boring machine

Abstract

Polynomial regression (PR) has been widely used to model simulations and experimental engineering data. The regression relationship of engineering data often varies greatly in different sub-sample spaces, making it difficult to evaluate accurately using a unified PR model. To solve this issue, a decision tree-assisted polynomial regression (DTPR) model is proposed in this article, in which a new decision tree is designed to partition the sample space according to the regression relationship. To make a prediction for a new sample, it is first compared from the root node of the decision tree until it reaches a leaf node; then, the output is obtained through the PR model of the leaf node. The experimental results indicate that the proposed model can show competitive performance in both mathematical functions and engineering cases. The proposed model is applied to the cutting force analysis of cutters of a tunnel boring machine to demonstrate its advantages.