Abstract: Micromechanical modelling of dual phase (DP) steel is used to predict the macroscopic tensile properties. The DP steel is widely used due to its good formability characteristics. The DP steels are having high strength and high ductility for suitable application in automobile industries. For the micromechanical analysis of the DP steel, the representative volumetric element (RVE) is generated from the microstructure of the steel obtained using scanning electron microscope at a suitable magnification showing explicit ferrite and martensite grains. In the present work, a model is formulated using Teacher- Learner based optimization (TLBO) algorithm to generate the artificial microstructure of the DP590 steel. The micromechanical modeling is performed to get the tensile macroscopic response. The model is able to predict the artificial microstructure and the tensile stress-strain of the DP590 steel with a reasonable accuracy.

• Publication Date: 10-Dec-2017
• DOI: 10.15224/978-1-63248-140-5-38
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Abstract: To predict the system accuracy, we should calculate the measure of the muzzle velocity error and elevation difference according to 1 m/s change of muzzle velocity. The magnitude of the muzzle velocity may be obtained by weighed sum of three quantities: the percentage errors of projectile weight, propellant weight and the volume of the chamber. The elevation difference according to 1 m/s change of muzzle velocity can be obtained from the multivariable function. The inputs of the multivariable function are: the distance that the bullet falls due to gravity, the distance from the launch position to the target, muzzle velocity, tangent angle from the launch position to the target. The error on the target due to muzzle velocity error can be a product of two quantities: the measure of the muzzle velocity error and elevation difference according to 1 m/s change of muzzle velocity. To predict the system accuracy, we use the accuracy function having the standard deviation and then we may obtain

• Publication Date: 10-Dec-2017
• DOI: 10.15224/978-1-63248-140-5-39
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