Determining the stress intensity factor of a material with an artificial neural network from acoustic emission measurements

被引：32
作者：
Kim, KB [1 ]
Yoon, DJ [1 ]
Jeong, JC [1 ]
Lee, SS [1 ]
机构：
[1] Korea Res Inst Stand & Sci, Ctr Safety Measurement, NDE Grp, Taejon 305600, South Korea
来源：
NDT & E INTERNATIONAL | 2004年 / 37卷 / 06期
关键词：
acoustic emission; artificial neural network; stress intensity factor; fatigue crack propagation;
D O I：
10.1016/j.ndteint.2003.08.007
中图分类号：
TB3 [工程材料学];
学科分类号：
0805 ; 080502 ;
摘要：
An artificial neural (ANN) network was trained to recognize the stress intensity factor in the interval from microcrack to fracture from acoustic emission (AE) measurements on compact tension specimens. The specimens were made from structural steel SWS490B whilst the ANN had a 5-14-1 structure. The number of neurons in the input layers was five inputs of the AE parameters such as ring-down counts, rise time, energy, event duration and peak amplitude. The performance of the ANN was tested using a specific set of the AE data. The ANN is a promising tool for predicting the stress intensity factor of material using AE data. (C) 2004 Elsevier Ltd. All rights reserved.
引用
收藏
页码：423 / 429
页数：7
相关论文
共 30 条
  • [1] ALMEIDA A, 1995, MATER EVAL, V53, P76
  • [2] Anderson TL., 1994, FRACTURE MECH FUNDAM, P41
  • [3] BARGA R, 1990, P SPIE INT SOC OPT E, P545
  • [4] STUDY OF FATIGUE-CRACK CHARACTERISTICS BY ACOUSTIC-EMISSION
    BERKOVITS, A
    FANG, DN
    [J]. ENGINEERING FRACTURE MECHANICS, 1995, 51 (03) : 401 - &
  • [5] CHEN HL, 1996, STRUCTURAL MAT TECHN, P317
  • [6] CHEN HL, 1992, 4 INT S AC EM COMP M, P273
  • [7] CICHOCKI A, 1993, NEURAL NETWORK OPTIM, P122
  • [8] The use of the neural networks in the recognition of the austenitic steel types
    Ciocan, R
    Petulescu, P
    Ciobanu, D
    Roth, DJ
    [J]. NDT & E INTERNATIONAL, 2000, 33 (02) : 85 - 89
  • [9] Draper N. R., 1966, APPL REGRESSION ANAL
  • [10] FREMAN JA, 1991, NEURAL NETWORKS ALGO, P89
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