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

Kılıç, Namık; Eki̇ci̇, Bülent; Hartomacıoğlu, Selim

doi:10.1016/j.dt.2014.12.001

Cited by 39 publications

(9 citation statements)

References 31 publications

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“…The whole model contains 21616 nodes and 1695 elements, and the mass is 8 g. The finite element model of fragmentation [23] is using AISI 4340 steel. The whole model contains 14283 nodes and 12584 elements with a mass of 1.13 g. The rifle bullet finite element model [24] is using a 7.62 mm rifle projectile whose warhead is divided into three parts: armor, steel core, and lead core. The whole model contains 8640 nodes and 5389 elements with a mass of 10.98 g. The finite element model diagrams are shown in Figure 1(d).…”

Section: Methodsmentioning

confidence: 99%

Study on Behind Helmet Blunt Trauma Caused by High-Speed Bullet

Cai

Huang

Xia

et al. 2020

Applied Bionics and Biomechanics

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The mechanism of Behind Helmet Blunt Trauma (BHBT) caused by a high-speed bullet is difficult to understand. At present, there is still a lack of corresponding parameters and test methods to evaluate this damage effectively. The purpose of the current study is therefore to investigate the response of the human skull and brain tissue under the loading of a bullet impacting a bullet-proof helmet, with the effects of impact direction, impact speed, and impactor structure being considered. A human brain finite element model which can accurately reconstruct the anatomical structures of the scalp, skull, brain tissue, etc., and can realistically reflect the biomechanical response of the brain under high impact speed was employed in this study. The responses of Back Face Deformation (BFD), brain displacement, skull stress, and dura mater pressure were extracted from simulations as the parameters reflecting BHBT risk, and the relationships between BHBT and bullet-proof equipment structure and performance were also investigated. The simulation results show that the frontal impact of the skull produces the largest amount of BFD, and when the impact directions are from the side, the skull stress is about twice higher than other directions. As the impact velocity increases, BFD, brain displacement, skull stress, and dura mater pressure increase. The brain damage caused by different structural bullet bodies is different under the condition of the same kinetic energy. The skull stress caused by the handgun bullet is the largest. The findings indicate that when a bullet impacts on the bullet-proof helmet, it has a higher probability of causing brain displacement and intracranial high pressure. The research results can provide a reference value for helmet optimization design and antielasticity evaluation and provide the theoretical basis for protection and rescue.

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Section: Methodsmentioning

confidence: 99%

Study on Behind Helmet Blunt Trauma Caused by High-Speed Bullet

Cai

Huang

Xia

et al. 2020

Applied Bionics and Biomechanics

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“…Using an artificial neural network (ANN) trained on a set of experimental data, predictions can be classified via a pass/fail bifurcation scheme. Results from numerical models can also be used, especially for uncommon HVI scenarios, to build a more comprehensive database, i.e., using a hybrid approach, which has been successful for highvelocity ballistic applications [56]. A division of the database into training and test data can then be assigned.…”

Section: Predictive Modelsmentioning

confidence: 99%

Hypervelocity Impacts on Satellite Sandwich Structures—A Review of Experimental Findings and Predictive Models

Carriere

Cherniaev

2021

Applied Mechanics

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Sandwich panels are widely used in the design of unmanned satellites and, in addition to having a structural function, can often serve as shielding, protecting the satellites’ equipment from hypervelocity impacts (HVI) of orbital debris and micrometeoroids. This paper provides a comprehensive review of experimental studies in the field of HVI on sandwich panels with honeycomb- and open-cell foam cores, as well as an examination of available predictive models for the assessment of the panels’ ballistic limits. The emphasis of the review is placed on: (i) identifying gaps in the existing experimental database and the appropriate directions for its further expansion; and (ii) understanding the limitations of the available predictive models and the potential for their improvement.

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“…In order for the classifier to perform better, there is a need for transforming the feature values into homogenous and well behaved values that yield numerical stability [16], [17]. This is done by determining the highest value of each attribute.…”

Section: B Normalization Of Attributesmentioning

confidence: 99%

Heart Diseases Diagnosis Using Neural Networks Arbitration

Olaniyi¹,

Oyedotun²,

Khashman³

2015

IJISA

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There is an increase in death rate yearly as a result of heart diseases. One of the major factors that cause this increase is misdiagnoses on the part of medical doctors or ignorance on the part of the patient. Heart diseases can be described as any kind of disorder that affects the heart. In this research work, causes of heart diseases, the complications and the remedies for the diseases have been considered. An intelligent system which can diagnose heart diseases has been implemented. This system will prevent misdiagnosis which is the major error that may occur by medical doctors. The dataset of statlog heart disease has been used to carry out this experiment. The dataset comprises attributes of patients diagnosed for heart diseases. The diagnosis was used to confirm whether heart disease is present or absent in the patient. The datasets were obtained from the UCI Machine Learning. This dataset was divided into training, validation set and testing set, to be fed into the network. The intelligent system was modeled on feed forward multilayer perceptron, and support vector machine. The recognition rate obtained from these models were later compared to ascertain the best model for the intelligent system due to its significance in medical field. The results obtained are 85%, 87.5% for feedforward multilayer perceptron, and support vector machine respectively. From this experiment we discovered that support vector machine is the best network for the diagnosis of heart disease.

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

Cited by 39 publications

References 31 publications

Study on Behind Helmet Blunt Trauma Caused by High-Speed Bullet

Study on Behind Helmet Blunt Trauma Caused by High-Speed Bullet

Hypervelocity Impacts on Satellite Sandwich Structures—A Review of Experimental Findings and Predictive Models

Heart Diseases Diagnosis Using Neural Networks Arbitration

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