文献详情
An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation
文献类型期刊
作者Zhang, Wei[1];Bao, Zhangmin[2];Jiang, Shan[3];He, Jingjing[4]
机构
通讯作者Zhang, W (reprint author), Beihang Univ, Sch Reliabil & Syst Engn, Beijing 100089, Peoples R China.
2016
期刊名称MATERIALS影响因子和分区
来源信息年:2016  卷:9  期:6  
9
期刊信息MATERIALS影响因子和分区  ISSN:1996-1944
6
关键词fatigue crack growth; artificial neural network; nonlinear multivariable function; retardation; loading interaction
增刊正刊
摘要In the aerospace and aviation sectors, the damage tolerance concept has been applied widely so that the modeling analysis of fatigue crack growth has become more and more significant. Since the process of crack propagation is highly nonlinear and determined by many factors, such as applied stress, plastic zone in the crack tip, length of the crack, etc., it is difficult to build up a general and flexible explicit function to accurately quantify this complicated relationship. Fortunately, the artificial neural network (ANN) is considered a powerful tool for establishing the nonlinear multivariate projection which shows potential in handling the fatigue crack problem. In this paper, a novel fatigue crack calculation algorithm based on a radial basis function (RBF)-ANN is proposed to study this relationship from the experimental data. In addition, a parameter called the equivalent stress intensity factor is also employed as training data to account for loading interaction effects. The testing data is then placed under constant amplitude loading with different stress ratios or overloads used for model validation. Moreover, the Forman and Wheeler equations are also adopted to compare with our proposed algorithm. The current investigation shows that the ANN-based approach can deliver a better agreement with the experimental data than the other two models, which supports that the RBF-ANN has nontrivial advantages in handling the fatigue crack growth problem. Furthermore, it implies that the proposed algorithm is possibly a sophisticated and promising method to compute fatigue crack growth in terms of loading interaction effects.
收录情况SCIE(WOS:000378630600082)  EI(20162602544524)  PubMed(28773606)  
所属部门可靠性与系统工程学院
DOI10.3390/ma9060483
百度学术An Artificial Neural Network-Based Algorithm for Evaluation of Fatigue Crack Propagation Considering Nonlinear Damage Accumulation
语言外文
ISSN1996-1944
被引频次33
人气指数59
浏览次数59
基金National Natural Science Foundation of China [51405009]; Fundamental Research Funds for the Central Universities
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