Finite element and generalized regression neural network modelling of multiple cracks growth under the influence of multiple crack parameters

IF 1.7 4区 材料科学 Q3 MATERIALS SCIENCE, MULTIDISCIPLINARY Multidiscipline Modeling in Materials and Structures Pub Date : 2023-07-27 DOI:10.1108/mmms-03-2023-0105
M. I. P. Hidayat, A. D. Pramata, Primaadi Airlangga
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Abstract

PurposeThis study presents finite element (FE) and generalized regression neural network (GRNN) approaches for modeling multiple crack growth problems and predicting crack-growth directions under the influence of multiple crack parameters.Design/methodology/approachTo determine the crack-growth direction in aluminum specimens, multiple crack parameters representing some degree of crack propagation complexity, including crack length, inclination angle, offset and distance, were examined. FE method models were developed for multiple crack growth simulations. To capture the complex relationships among multiple crack-growth variables, GRNN models were developed as nonlinear regression models. Six input variables and one output variable comprising 65 training and 20 test datasets were established.FindingsThe FE model could conveniently simulate the crack-growth directions. However, several multiple crack parameters could affect the simulation accuracy. The GRNN offers a reliable method for modeling the growth of multiple cracks. Using 76% of the total dataset, the NN model attained an R2 value of 0.985.Research limitations/implicationsThe models are presented for static multiple crack growth problems. No material anisotropy is observed.Practical implicationsIn practical crack-growth analyses, the NN approach provides significant benefits and savings.Originality/valueThe proposed GRNN model is simple to develop and accurate. Its performance was superior to that of other NN models. This model is also suitable for modeling multiple crack growths with arbitrary geometries. The proposed GRNN model demonstrates its prediction capability with a simpler learning process, thus producing efficient multiple crack growth predictions and assessments.
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多裂纹参数影响下多裂纹扩展的有限元与广义回归神经网络建模
目的本研究提出了有限元(FE)和广义回归神经网络(GRNN)方法,用于建模多个裂纹扩展问题,并预测多个裂纹参数影响下的裂纹扩展方向。设计/方法/方法为了确定铝试样中的裂纹扩展方向,检查了代表一定程度裂纹扩展复杂性的多个裂纹参数,包括裂纹长度、倾角、偏移和距离。针对多裂纹扩展模拟开发了有限元方法模型。为了捕捉多个裂纹扩展变量之间的复杂关系,GRNN模型被开发为非线性回归模型。建立了六个输入变量和一个输出变量,包括65个训练数据集和20个测试数据集。发现有限元模型可以方便地模拟裂纹的扩展方向。然而,多个裂纹参数可能会影响模拟精度。GRNN为多个裂纹的扩展建模提供了一种可靠的方法。使用总数据集的76%,NN模型获得了0.985的R2值。研究限制/含义这些模型是针对静态多裂纹扩展问题提出的。未观察到材料各向异性。实际意义在实际裂纹扩展分析中,NN方法提供了显著的好处和节约。独创性/价值所提出的GRNN模型开发简单且准确。其性能优于其他神经网络模型。该模型也适用于对具有任意几何形状的多个裂纹生长进行建模。所提出的GRNN模型通过更简单的学习过程证明了其预测能力,从而产生有效的多重裂纹扩展预测和评估。
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来源期刊
CiteScore
3.70
自引率
5.00%
发文量
60
期刊介绍: Multidiscipline Modeling in Materials and Structures is published by Emerald Group Publishing Limited from 2010
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