Bagging-gradient boosting decision tree based milling cutter wear status prediction modelling

Abstract

Aiming to monitor wear condition of milling cutters in time and provide tool change decisions to ensure manufacturing safety and product quality, a tool wear monitoring model based on Bagging-Gradient Boosting Decision Tree (Bagging-GBDT) is proposed. In order to avoid incomplete tool state information contained in a single domain feature parameter, a multi-domain combination method is used to extract candidate characteristic parameter sets from time domain, frequency domain, and time–frequency domain. Then top 21 significant features are screened by eXtreme Gradient Boosting selection method. Synthetic Minority Oversampling Technique technology is integrated during feature selection to overly sample feature vectors, so that wear condition categories can be well balanced. Bagging idea is then introduced for parallel calculation of the gradient boosting decision tree and to improve its generalization ability. A Bagging-GBDT milling cutter wear condition prediction model is constructed and verified by public ball-end milling data set. Experiments show that random features and training samples selection can effectively improve prediction performance and generalization ability of prediction model. Our Bagging-GBDT model gains F1 score of 0.99350, which is 0.2% and 13.2% higher than the random forest algorithm and basic GBDT model, respectively.

Machine-Learning-Based Framework for Prediction of the Long-Term Field Performance of Asphalt Concrete Overlays in a Hot and Humid Climate

“…where l ( y i , y ^ i ) is the loss function measuring the difference between the prediction ( y ^ i ) and target ( y i ) values and Ω ( f T ) is a regularization term that controls the model complexity and helps in smoothing the trained weight values to prevent overfitting. Here, Ω ( f T ) is calculated using Equation 11 ( 19 ):…”

“…Relative importance ( 19 ) is a statistical measure defined as the percentage contribution of each input variable to the model when the variables are dependent and not directly manipulated. The relative importance of a variable is calculated as the total gain from this variable across all trees and normalized such that all variables add up to 100 ( 21 ).…”