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

• URL: http://arxiv.org/abs/2206.08011v1
• Date: Thu, 16 Jun 2022 09:14:26 GMT
• Title: Hardness prediction of age-hardening aluminum alloy based on ensemble learning
• Authors: Zuo Houchen (1), Jiang Yongquan (2), Yang Yan (2), Liu Baoying (2) and Hu Jie (1) ((1) State Key Labratory of Traction Power, Southwest Jiaotong University, Chengdu, China, (2) School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu, China.)
• Abstract summary: alloy are used to input composition, aging conditions (time and temperature) and predict its hardness. Experiments 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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