Investigation on wear prediction model of dental restoration material based on ensemble learning

Abstract

The wear test was performed with TC4 alloy, by changing the normal bite force, sliding frequency and cycles, in artificial saliva. Taking the two results for testing samples and the others for training samples, the radial basis function (RBF) and multilayer perceptron (MLP) neural network model and least mean square (LMS) and K* model were built for predicting wear loss respectively. Then an ensemble learning model was built which integrated all the single models based on the weight determined by mean absolute error. Compared with the testing results, it was obtained that the error for ensemble learning model was between 3 and 4%. Also, its prediction error rate reduced over 50% for the tenth group data, which embodied good stability and high precision on predicting the wear loss for dental restorative material.