论文标题
基于合奏学习的硬性铝合金的硬度预测
Hardness prediction of age-hardening aluminum alloy based on ensemble learning
论文作者
论文摘要
随着人工智能的快速发展，材料数据库和机器学习的结合促进了材料信息学的进步。因为铝合金广泛用于许多领域，因此预测铝合金的性质是很重要的。在本文中，使用Al-Cu-Mg-X（X：Zn，Zr等）的数据来输入组成，衰老条件（时间和温度）并预测其硬度。分别提出了基于自动机器学习和引入深度神经网络二级学习者的注意机制的集合学习解决方案。实验结果表明，选择正确的二级学习者可以进一步提高模型的预测准确性。该手稿介绍了基于深神经网络的二级学习者的注意机制，并获得了具有更好性能的融合模型。最佳模型的R平方为0.9697，MAE为3.4518hv。
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.
