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Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning

Changsheng Zhu1,2,*, Borui Zhao1, Naranjo Villota Jose Luis1, Zihao Gao1, Li Feng3

1 College of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China
2 State Key Laboratory of Gansu Advanced Processing and Recycling of Non-Ferrous Metal, Lanzhou University of Technology, Lanzhou, 730050, China
3 College of Materials Science and Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding Author: Changsheng Zhu. Email: email

Computers, Materials & Continua 2023, 76(1), 49-64. https://doi.org/10.32604/cmc.2023.036729

Abstract

In this study, a phase field model is established to simulate the microstructure formation during the solidification of dendrites by taking the Al-Cu-Mg ternary alloy as an example, and machine learning and deep learning methods are combined with the Kim-Kim-Suzuki (KKS) phase field model to predict the quasi-phase equilibrium. The paper first uses the least squares method to obtain the required data and then applies eight machine learning methods and five deep learning methods to train the quasi-phase equilibrium prediction models. After obtaining different models, this paper compares the reliability of the established models by using the test data and uses two evaluation criteria to analyze the performance of these models. This work find that the performance of the established deep learning models is generally better than that of the machine learning models, and the Multilayer Perceptron (MLP) based quasi-phase equilibrium prediction model achieves the best performance. Meanwhile the Convolutional Neural Network (CNN) based model also achieves competitive results. The experimental results show that the model proposed in this paper can predict the quasi-phase equilibrium of the KKS phase-field model accurately, which proves that it is feasible to combine machine learning and deep learning methods with phase-field model simulation.

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APA Style
Zhu, C., Zhao, B., Luis, N.V.J., Gao, Z., Feng, L. (2023). Quasi-phase equilibrium prediction of multi-element alloys based on machine learning and deep learning. Computers, Materials & Continua, 76(1), 49-64. https://doi.org/10.32604/cmc.2023.036729
Vancouver Style
Zhu C, Zhao B, Luis NVJ, Gao Z, Feng L. Quasi-phase equilibrium prediction of multi-element alloys based on machine learning and deep learning. Comput Mater Contin. 2023;76(1):49-64 https://doi.org/10.32604/cmc.2023.036729
IEEE Style
C. Zhu, B. Zhao, N.V.J. Luis, Z. Gao, and L. Feng "Quasi-Phase Equilibrium Prediction of Multi-Element Alloys Based on Machine Learning and Deep Learning," Comput. Mater. Contin., vol. 76, no. 1, pp. 49-64. 2023. https://doi.org/10.32604/cmc.2023.036729



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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