Predicting and optimizing coupling effect in magnetoelectric multi-phase composites based on machine learning algorithm. (1st September 2021)

• Record Type: Journal Article
• Authors: Zhu, Weihao; Yang, Chen; Huang, Bin; Guo, Yan; Xie, Longtao; Zhang, Yangyang; Wang, Ji
• Is Part Of: Composite structures. Volume 271(2021)
• Publication Date: 2021-09-01
• Subjects: Machine learning -- Magnetoelectric coupling -- Multi-phase composite -- Finite Element method -- Optimization; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2021.114175
• Languages: English
• ISSNs: 0263-8223

Abstract

Abstract: In this paper, we present a machine learning (ML) method to search for geometric patterns of magnetoelectric multi-phase composites with optimal magnetoelectric coupling properties. The 2D finite element method is used to calculate the coupling coefficients and build the database for the training and testing of ML algorithms. Both the convolution neural network (CNN) and artificial neural network (ANN) algorithms are used as ML algorithms in this work. By investigating the effects of network parameters, such as training data density, iteration number and batch size, we construct the networks with proper parameters and good prediction accuracy. We present the predicted geometric patterns by two methods, CNN and ANN, and compare them with the FEM patterns. Two types of magnetoelectric composites, two -volume fractions of magnetostrictive phase and two system sizes are considered to investigate and improve the prediction efficiency. The presented results show that by using the ML methods, it can well predict the coupling effect and rank optimal patterns. The results also prove the feasibility that by using the ML method, we can accurately predict the coupling performance with very limited data. We aim to predict the optimal patterns instead of the best pattern. Therefore, this work demonstrates the feasibility and offers a new perspective way for the design and optimization of magnetoelectric multi-phase composites by using the ML algorithm.