Tip Deflection Determination of a Barrel for the Effect of an Accelerating Projectile Before Firing Using Finite Element and Artificial Neural Network Combined Algorithm

Abstract

For realistic applications, design and control engineers have limited modelling options in dealing with some vibration problems that hold many nonlinearity such as non-uniform geometry, variable velocity loadings, indefinite damping cases, etc. For these reasons numerous time consuming experimental studies at high costs must be done for determining the actual behaviour such nonlinear systems. However, using advantages of multiple computational methods like Finite Element Method (FEM) together with an Artificial Intelligence (ANN), many complicated engineering problems can be handled and solved to some extent. This study, proposes a new collective method to deal with the nonlinear vibrations of the barrels in order to fulfil accurate shooting expectancy. Using known analytical methods, in practical, to determine dynamic behaviour of the barrel beam is not possible for all conditions of firing that include numerous varieties of ammunition for different purposes, and each projectile of different ammunition has different mass and exit velocity. In order to cover all cases this study proposes a new method that combines a precise FEM with ANN, and can be used for determining the exact dynamic behaviour of a barrel for some cases and then for precisely predicting the behaviour for all other possible cases of firing. In this study, the whole nonlinear behaviour of an antiaircraft barrel were obtained with 3.5% accuracy errors by ANN trained by FEM using calculated analysis results of ammunitions for a particular range. The proposed FEM-ANN combined method can be very useful for design and control engineers in design and control of barrels in order to compensate the effect of nonlinear vibrations of a barrel for achieving a higher shooting accuracy; and can reduce high-cost experimental works.

Fault Detection in Cracked Structure under Moving Load using RNNs based Approach

“…Jena and Parhi [24] have determined the responses of different types of beam structures subjected to moving load under variable damage conditions of structures. Koc et al [25] have combined the finite element and NN method to predict the end deflection of a barrel to study the consequences of an accelerating projectile. Back propagation algorithm was implemented in the NN model.…”

Effects of Projectile and Gun Parameters on the Dispersion

“…Gun-projectile interaction has been investigated through large amount of researches. Experimental setups, analytical and finite element models have been developed to simulate the gun-projectile interaction and to examine the sensitivity of parameters on the dispersion [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22] .…”

“…The dynamic interaction between the gun barrel and accelerating projectile during firing has been analytically examined 19,20 . The effects of projectile mass, exit velocity, acceleration effects and barrel inclination angle on the muzzle displacements have been determined.…”

A Numerical Study on the Effect of Position and Number of Openings on the Performance of Composite Steel Shear Walls

“…The authors of this research believed that using neural network and combining it with FEM can solve many complicated engineering problems. In this research, the nonlinear antiaircraft barrel's behavior was obtained using a trained artificial neural network (ANN) and also FEM to show that the combination of these two methods can predict the structure's behavior correctly [14]. Lai et al used a neural network to estimate soil deformation in tunneling and they believed that the combination of the neural network with other research methods like FEM could solve many engineering problems [15].…”