A new tool based on artificial neural networks for the design of lightweight ceramic–metal armour against high-velocity impact of solids

Fernández-Fdz, D.; Zaera, R.

doi:10.1016/j.ijsolstr.2008.08.009

Cited by 21 publications

(9 citation statements)

References 27 publications

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“…Although MLP is extensively used in all engineering fields, the number of studies performed on ballistics is rare. Fernandez-Fdz and Zarea [30] developed a two levels MLP model to establish a toll for lightweight ceramic-aluminum armor design. Their model was based on hyperbolic tangent and logistic activation functions and 200 impact cases were created with FE simulations for learning, cross validation and testing.…”

Section: Neural Network Model Developmentmentioning

confidence: 99%

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Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

2015

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Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM) in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN) analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP) three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s) is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

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Section: Neural Network Model Developmentmentioning

confidence: 99%

“…In literature there is not a specific rule to determine amount of training data set. Fernandez-Fdz and Zarea [30] have randomly chosen training data set to be 85%. Renahan et al [31] has also chosen training data randomly and amount is approximately 80% of data set.…”

Section: Definitionmentioning

confidence: 99%

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

2015

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“…In literature, there can be found numerous applications of numerical modelling in the study of the multilayer structures behaviour. For this purpose, an artificial neural networks technology [13,15] has been also used recently. It is a comparative technology in comparison with numerical modelling since it shortens the time of a problem solution.…”

Section: Introductionmentioning

confidence: 99%

Ballistic resistance tests of multi-layer protective panels

Godzimirski¹,

Janiszewski²,

Rośkowicz³

et al. 2015

EiN

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“…For instance, Umbrello et al [8] used this combined method to predict machining induced residual stresses during hard turning; and Chamekh et al [9] proposed a hybrid procedure for the identification of HILL anisotropic parameters based on deep drawing of a cylindrical cup. Fernández-Fdz and Zaera [10] used the combined approach to predict the penetration behaviour of ceramic targets under the impact of different projectiles.…”

Section: Introductionmentioning

confidence: 99%

Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests

Muñoz-Sánchez

Farias

Soldani

et al. 2010

Int J Adv Manuf Technol

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Accuracy of numerical models based in finite elements (FE), extensively used for simulation of cutting processes, depends strongly on the identification of proper material parameters. Experimental identification of the constitutive law parameters for simulation of cutting processes involves unsolved problems such as the complex testing techniques or the difficulty to reproduce the stress triaxiality state during cutting. This work proposes a methodology for the inverse identification of the material parameters from cutting test. Two hybrid approaches are compared. One of them based on FE and artificial neural networks (ANN). The other one based on FE and local polynomial regression (LPR). Firstly, a FE model is validated with experimental data. Then, ANN and LPR are trained with FE simulations. Finally, the estimated ANN and LPR models are used for the inverse identification of material parameters. This identification is solved as an optimization problem. The FE/LPR approach shows good performance, outperforming the FE/ANN approach.

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A new tool based on artificial neural networks for the design of lightweight ceramic–metal armour against high-velocity impact of solids

Cited by 21 publications

References 27 publications

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Ballistic resistance tests of multi-layer protective panels

Hybrid FE/ANN and LPR approach for the inverse identification of material parameters from cutting tests

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