Property prediction of AZ80 magnesium alloy: an extreme learning machine model optimized by a new improved sparrow search algorithm

ABSTRACT

The mechanical properties of magnesium alloy in different molding stages are very important factors to determine its application approaches in engineering. In order to ensure the prediction accuracy of mechanical properties, a TCMSSA-ELM model, which is a hybrid of the sparrow search algorithm (SSA) optimized by the tent chaotic mapping (TCM) algorithm and the extreme learning machine (ELM), is proposed in this study, and the stresses of AZ80 magnesium alloy are predicted by the model through a 812-record dataset. The predicting results indicate that TCMSSA improves the accuracy of ELM model. Compared with ELM model, the data points formed by experimental values of stress and the predicted ones by TCMSSA-ELM model are closer to the ideal 45° line, the average determination coefficient rises by 1.43%, and the average root mean squared error (RMSE) decreases by nearly 61.96%, implying that TCMSSA-ELM model accurately reflects the influence rule of thermodynamic parameters on stress. The novelty of this study is that TCM is used to optimize the population initialization of SSA, which enables SSA to have a higher global search ability, and thus optimizes the weight and threshold selection of ELM, then making TCMSSA-ELM have higher prediction accuracy than other improved ELM models.

Keywords:
Sparrow search algorithm; Extreme learning machine; TCMSSA-ELM model; AZ80 magnesium alloy; Mechanical property prediction