Open Access

ARTICLE

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

Changsheng Zhu^1,2,*, 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:

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

Received 10 October 2022; Accepted 29 December 2022; Issue published 08 June 2023

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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Keywords

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