An investigation of the constitutive behavior of Armox 500T steel and armor piercing incendiary projectile material

Iqbal, M.A.; Senthil, K.; Sharma, Prince; Gupta, Neelam

doi:10.1016/j.ijimpeng.2016.05.017

Cited by 105 publications

(48 citation statements)

References 31 publications

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“…The JC model has been used to describe the behaviour of various metals such as iron [18,20], copper [18,20], steels [18,[20][21][22][23] or lightweight alloys [24][25][26]. In practice, if neglecting thermal softening, the identification of JC constitutive parameters includes two steps which rely on statically determinate approaches.…”

Section: Johnson-cook Model For Rate-dependent Elasto-plastic Materiamentioning

confidence: 99%

A computational approach to design new tests for viscoplasticity characterization at high strain-rates

et al. 2019

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Rate-dependent behaviour characterization of metals at high strain rate remains challenging mainly because of the strong hypotheses when tests are processed with statically determinate approaches. As a non-standard methodology, Image-Based Inertial Impact (IBII) test has been proposed to take advantage of the dynamic Virtual Fields Method (VFM) which enables the identification of constitutive parameters with strain and acceleration fields. However, most of the test parameters (e.g. projectile velocity, specimen geometry) are not constrained. Therefore, an FE-based approach is addressed to optimize the identification over a wide range of strain and strain-rate, according to two design criteria: (1)-the characterized viscoplastic spectra (2)-the identifiability of the parameters. Whereas the first criterion is assessed by processing the FEA simulations, the second is rated extracting material parameters using synthetic images to input the VFM. Finally, uncertainties regarding the identification of material constants are quantified for each IBII test configuration and different camera performances.

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Section: Johnson-cook Model For Rate-dependent Elasto-plastic Materiamentioning

confidence: 99%

A computational approach to design new tests for viscoplasticity characterization at high strain-rates

et al. 2019

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“…In the study, Armox 500T and Aluminum 7075-T6 materials were used as the target plates and their resistance under the low velocity (drop weight) and high velocity (ballistic impact) loads are investigated. The material properties of Armox 500T are taken from a reference study which was carried by Iqbal et al [31]. The material properties of Armox-500T obtained in this detail study is listed in Table 1.…”

Section: Materials Properties Of Platesmentioning

confidence: 99%

“…Aluminum 7075-T6 is the mostly used material in the light weight impact applications and it's material properties are taken from material library of ANSYS [32] and the material parameters are given in Table 2. In the ANSYS, using this model, yield stress can be defined as [31];…”

Section: Materials Properties Of Platesmentioning

confidence: 99%

Damage resistance investigation of Armox 500T and Aluminum 7075-T6 plates subjected to drop-weight and ballistic impact loads

Yeter

2019

Sakarya University Journal of Science

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The main objective of this paper is to investigate damage resistance of Armox 500T and Aluminum 7075-T6 plates subjected to drop-weight and ballistic impact loads. Investigating the behavior of structures under the low or the high velocity impact loads is an important research topic. The study of materials and their combinations provides fundamental understanding of many engineering structures. In this study, firstly drop weight and ballistic impact resistance of the Armox-500T and Al7075-T6 materials was examined. Ballistic impact analyses were carried out using 7.62 API projectiles with an initial velocity of 800 m/s. During the drop-weight analyses, the drop of 5.5 kg weight from the 800 mm distance was modeled. The situations at which target plates of different thickness can be fully penetrated or not to be fully penetrated by the projectile, the final (residual) velocities in the fully penetrated plates and the amount of energy absorbed by the target plates were investigated. 6.72 API projectiles with an initial velocity of 800 m/s could not fully penetrated the 10 mm Armox-500T target and 26 mm Al7075-T6 target. When drop-weight results are concerned, the maximum impact loads of the Armox-500T target is higher than the Al7075-T6, and the deformation amount is less. In addition, 10 different hybrid models, which consist of various combination of Armox 500T and Al7075-T6 materials in different thicknesses and orientations, have been defined. These models were compared with each other and models that are more resistant to ballistic impact loads were determined. M4, M7, M9, and M10 models were found to be more resistant to the ballistic impact loads than other models.

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“…It acquires these specific properties due to its chemical composition and a specific production process, finalized by very rapid quenching and tempering. The chemical purity in combination with very rapid cooling brings good toughness of material despite of tempering at very low temperature [8][9][10].…”

Section: Figmentioning

confidence: 99%

Prediction of Abrasive Water Jet Cutting Parameters Using Artificial Neural Network

Elattar

Mahdy²,

Sonbol

2018

The International Conference on Applied Mechanics and Mechanica

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This work presents a new predictive model of abrasive water-jet (AWJ) machining of ARMOX shielding steel plate of 7.6 mm thick. The model was developed to predict some interesting process parameters from process variables. As AWJ is a complicated multi input multi output machining process. The model is developed using artificial neural network (ANN). A feed forward neural network based on back propagation was made up of 4 input neurons, 1 hidden layer with 10 hidden neurons and 2 output neurons. The ANN training set was generated by extensive experimental work. The tests considered four process variables. The studied AWJ process variables are traverse speed (T), waterjet pressure (P), standoff distance (s), and abrasive flow rate (m a ). The considered process parameters are surface roughness (R a ) and material removal rate (MRR). The ANN model was trained and tested. The ANN succeeded to model the AWJ process by extracting the process parameters from process variables with a regression factor above 90%. This paper is a step forward to model and control the AWJ machining process. KEYWORDSAbrasive water jet (AWJ), Armox, artificial neural network (ANN), surface roughness (Ra), material removal rate (MRR) ---

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An investigation of the constitutive behavior of Armox 500T steel and armor piercing incendiary projectile material

Cited by 105 publications

References 31 publications

A computational approach to design new tests for viscoplasticity characterization at high strain-rates

A computational approach to design new tests for viscoplasticity characterization at high strain-rates

Damage resistance investigation of Armox 500T and Aluminum 7075-T6 plates subjected to drop-weight and ballistic impact loads

Prediction of Abrasive Water Jet Cutting Parameters Using Artificial Neural Network

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