Approximations for Stress-Intensity Factors and Crack Propagation of Box Beams

被引:0
作者
Lo, Hung-Chieh [1 ]
Sunny, Mohammed R. [2 ]
Kapania, Rakesh K. [3 ]
Patil, Mayuresh J. [3 ]
机构
[1] Darfon Elect Corp, Human Interface Devices BG, Taoyuan 333426, Taiwan
[2] Indian Inst Technol Kharagpur, Aerosp Engn, Kharagpur 721302, W Bengal, India
[3] Virginia Polytech Inst & State Univ, Dept Aerosp & Ocean Engn, Blacksburg, VA 24061 USA
关键词
NEURAL-NETWORKS;
D O I
10.2514/1.J059520
中图分类号
V [航空、航天];
学科分类号
08 ; 0825 ;
摘要
The stress-intensity factors of box beams under torsion and crack propagation under torsion or/and bending moment are discussed here. This study is motivated by a previous work [1] that derived a closed-form expression of the mode II stress-intensity factor for thin-walled beams with a centered longitudinal crack and subjected to torsion. Naturally, the way in which mode I stress-intensity factors are influenced by torsion is of interest. The influence of parameters such as crack length, crack angle, width and depth of beam, wall thickness, and stiffener size on mode I and mode II stress-intensity factors has been studied using the finite element method and represented the data in a surrogate model using two approaches: 1) a Fourier-series-based correction factor, and 2) an artificial neural network. The crack propagation is also of interest. Specifically, the crack propagation study focuses on both the limit loadings and the angle in which the initial crack growth occurs.
引用
收藏
页码:1352 / 1360
页数:9
相关论文
共 10 条
  • [1] [Anonymous], 2010, ABAQUS SOFTWARE PACK
  • [2] Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, P303, DOI 10.1007/BF02551274
  • [3] Analytical Modeling of Cracked Thin-Walled Beams Under Torsion
    Dang, Thi D.
    Kapania, Rakesh K.
    Patil, Mayuresh J.
    [J]. AIAA JOURNAL, 2010, 48 (03) : 664 - 675
  • [4] ON THE APPROXIMATE REALIZATION OF CONTINUOUS-MAPPINGS BY NEURAL NETWORKS
    FUNAHASHI, K
    [J]. NEURAL NETWORKS, 1989, 2 (03) : 183 - 192
  • [5] An Abaqus implementation of the extended finite element method
    Giner, E.
    Sukumar, N.
    Tarancon, J. E.
    Fuenmayor, F. J.
    [J]. ENGINEERING FRACTURE MECHANICS, 2009, 76 (03) : 347 - 368
  • [6] Haykin S., 1994, Neural Networks: A Comprehensive Foundation, DOI [10.5555/975792.975796, DOI 10.5555/975792.975796]
  • [7] Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements
    Kim, KB
    Yoon, DJ
    Jeong, JC
    Lee, SS
    [J]. NDT & E INTERNATIONAL, 2004, 37 (06) : 423 - 429
  • [8] Neural networks for inverse problems in damage identification and optical imaging
    Kim, YY
    Kapania, RK
    [J]. AIAA JOURNAL, 2003, 41 (04) : 732 - 740
  • [9] Explicit formulation of SIF using neural networks for opening mode of fracture
    Kutuk, M. A.
    Atmaca, N.
    Guzelbey, I. H.
    [J]. ENGINEERING STRUCTURES, 2007, 29 (09) : 2080 - 2086
  • [10] Stress analysis around crack tips in finite strain problems using the extended finite element method
    Legrain, G
    Moës, N
    Verron, E
    [J]. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2005, 63 (02) : 290 - 314