Evaluation of the Elastic Modulus and Plateau Stress of a 2D Porous Aluminum Alloy Based on a Convolutional Neural Network

被引:7
作者
Sun, Jianhang [1 ]
Xu, Yepeng [1 ]
Wang, Lei [1 ]
机构
[1] Hohai Univ, Coll Mech & Mat, Nanjing 211100, Peoples R China
基金
中国国家自然科学基金;
关键词
porous metal; elastic modulus; plateau stress; convolutional neural network; forward prediction; inverse design; PREDICTION;
D O I
10.3390/met13020284
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Porous metals are a new ultra-light material with high specific stiffness, specific strength, and good energy absorption properties. The elastic modulus and plateau stress of porous metals are essential parameters. There have been many studies on the effects of the matrix material, porosity, and pore size on the elastic modulus and plateau stress of porous metals, but few studies can be found on the impact of pore arrangement. The pore arrangement of porous metals cannot be quantitatively described, and the design space of a porous metal structure under the same porosity is vast. With the powerful learning and prediction ability of neural networks, the influence of pore arrangement can be better understood. In this paper, a convolutional neural network was used to explore the impact of pore arrangement on both the elastic modulus and plateau stress of a porous aluminum alloy. Firstly, a finite element method was used to simulate the compression of a porous aluminum alloy to obtain a training sample library. Secondly, a convolutional neural network was built to positively predict the elastic modulus and plateau stress of the porous aluminum alloy. Partial samples were used to select the best training model from five convolutional neural network candidates. Dropout, Batch Normalization, and L2 regularization methods were used to alleviate the over-fitting phenomenon in training. All data in the database were then trained and predicted, and the predicted goodness of fit of the elastic modulus and plateau stress were 0.8785 and 0.5922, respectively. A search method based on the convolutional neural network was then used to iteratively search the database. Under the condition of using a small amount of data, the pore structure with the best elastic modulus and plateau stress in the database could be determined, and the inverse design of a structure with high elastic modulus and plateau stress could be realized.
引用
收藏
页数:13
相关论文
共 26 条
  • [1] Machine learning for predicting properties of porous media from 2d X-ray images
    Alqahtani, Naif
    Alzubaidi, Fatimah
    Armstrong, Ryan T.
    Swietojanski, Pawel
    Mostaghimi, Peyman
    [J]. JOURNAL OF PETROLEUM SCIENCE AND ENGINEERING, 2020, 184
  • [2] Cheng F.F., 2015, THESIS TAIYUAN U SCI
  • [3] [郭丽丽 Guo Lili], 2015, [计算机科学, Computer Science], V42, P28
  • [4] Accelerated Search and Design of Stretchable Graphene Kirigami Using Machine Learning
    Hanakata, Paul Z.
    Cubuk, Ekin D.
    Campbell, David K.
    Park, Harold S.
    [J]. PHYSICAL REVIEW LETTERS, 2018, 121 (25)
  • [5] Hou X.L., 2019, PACKAG ENG, V40, P86, DOI [10.19554/j.cnki.1001-3563.2019.19.012, DOI 10.19554/J.CNKI.1001-3563.2019.19.012]
  • [6] Ioffe S., 2015, PROC INT C MACH LEAR
  • [7] Jiang K., 2021, APPL SCI-BASEL, V35, P64, DOI [10.19860/j.cnki.issn1005-8249.2021.03.012, DOI 10.19860/J.CNKI.ISSN1005-8249.2021.03.012]
  • [8] Li J., 2019, Masters Thesis
  • [9] Liu N., 2018, THESIS CHINA ACAD EN
  • [10] Liu P., 2004, Introduction to Porous Materials