Hardness prediction of age-hardening aluminum alloy based on ensemble learning

TL;DR

This paper presents an ensemble learning approach incorporating attention mechanisms to accurately predict the hardness of age-hardening aluminum alloys, demonstrating high predictive performance with an R-Square of 0.9697.

Contribution

It introduces a novel ensemble learning model with an attention mechanism for aluminum alloy hardness prediction, improving accuracy over existing methods.

Findings

The proposed model achieves an R-Square of 0.9697.

Attention mechanisms enhance the secondary learner performance.

Ensemble learning improves prediction accuracy for alloy properties.

Abstract

With the rapid development of artificial intelligence, the combination of material database and machine learning has driven the progress of material informatics. Because aluminum alloy is widely used in many fields, so it is significant to predict the properties of aluminum alloy. In this thesis, the data of Al-Cu-Mg-X (X: Zn, Zr, etc.) alloy are used to input the composition, aging conditions (time and temperature) and predict its hardness. An ensemble learning solution based on automatic machine learning and an attention mechanism introduced into the secondary learner of deep neural network are proposed respectively. The experimental results show that selecting the correct secondary learner can further improve the prediction accuracy of the model. This manuscript introduces the attention mechanism to improve the secondary learner based on deep neural network, and obtains a fusion model with better performance. The R-Square of the best model is 0.9697 and the MAE is 3.4518HV.