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

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.

Keywords

• Nonlinear vibration modelling
• Vibration of continuous systems
• Artificial neural networks
• Gun barrels
• Finite element method