新闻中心


压电实验室团队发表基于机器学习的多相磁电复合材料优化最新研究成果
2021-06-19  


压电实验室团队发表基于机器学习的多相磁电复合材料优化最新研究成果


2021年6月18日】近日,压电实验室团队一篇题为《Predicting and Optimizing Coupling Effect in Magnetoelectric Multi-Phase Composites Based on Machine Learning Algorithm》的论文在Composite Structures发表。这项工作由黄斌副教授负责,研究工作提出了一种基于机器学习(ML)的方法研究磁电复合材料的磁电耦合效应以及预测优化的复合材料结构模式。机器学习算法在材料与结构的性能优化设计中具有巨大的应用潜力,是目前的研究热点。本文主要采用了二维有限元模型计算磁电耦合系数并建立数据库用于机器学习的网络训练和预测。论文采用了卷积神经网络CNN和人工神经网络ANN两种机器学习方法,并研究了网络参数,包括训练数据密度、迭代次数、批处理大小等对网络预测精度的影响。经CNNANN预测的磁电复合结构模式与有限元计算的结构模式进行了对比,结果表明本文训练的机器学习模型可以精确预测多相磁电复合材料的磁电耦合性能。文章还同时研究了两种类型的磁电复合材料(L-TL-L)、不同组成成分的磁电复合材料(磁致伸缩相占12.5%25%)、不同网格大小(8×816×16)等因素的影响。本文研究结果表明仅利用少量的数据进行网络训练,可以有效预测多相磁电复合材料的磁电耦合性能,预测一批优化的复合结构模式,可以为多相磁电复合材料与结构的制造提供了一种可行的设计和优化方案。这篇论文的第一作者为研究生朱为豪,通讯作者为指导教师黄斌副教授和王骥教授。



1 L-T型磁致伸缩相占12.5%时前20种优化结构:(a) FEM; (b) CNN; (c) ANN


2 L-T型磁致伸缩相占25%时前20种优化结构:(a) FEM; (b) CNN; (c) ANN

 

 

3 L-T型磁致伸缩相占12.5%16×16系统前20个优化结构:(a) FEM; (b) CNN; (c) ANN

 

4 文章摘要


论文链接https://doi.org/10.1016/j.compstruct.2021.114175

论文下载地址http://piezo.nbu.edu.cn/info/1041/2179.htm



PDL team published a paper about the Optimization of multiphase magnetoelectric composites based on Machine Learning


【June 18, 2021】Recently, a paper by Professor Bin Huang and his team of Piezoelectric Device Laboratory (PDL) entitled " Predicting and Optimizing Coupling Effect in Magnetoelectric Multi-Phase Composites Based on Machine Learning Algorithm " was published in Composite Structures. 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.  The first author of the paper is Weihao Zhu, a graduate student, and the corresponding author is Professor Bin Huang.

 

Paper website: https://doi.org/10.1016/j.compstruct.2021.114175

Paper download address:http://piezo.nbu.edu.cn/info/1041/2179.htm


 

上一条:宁波诺丁汉大学副教授杨建博士来访并作学术报告
下一条:胡海岩院士参加力学博士点建设座谈会
关闭窗口

更多消息请联系:

宁波大学压电器件技术实验室

联系人:王骥
联系电话:0574-8760 0467
E-mail:wangji@nbu.edu.cn

网站:http://piezo.nbu.edu.cn/

Powered by 压电器件技术重点实验室

管理登陆