Using neural networks to predict the effect of the preload location on the natural frequencies of a cantilever beam

With the evolution of computational power of computers in 20th century, neural networks (NNs) are becoming more popular in different engineering applications because of its ability to approximate static and dynamic, linear and non-linear, multi-dimensional systems. For example, they are used in industrial processes to automate assembly lines which increases its productivity and in automotive to reduce gas consumption of an engine. In this paper, NNs are utilized to reduce the computational power needed for finite element methods (FEM) simulations. A case study is taken for which NNs are used to predict the effect of the preload position and magnitude on the natural frequencies of the prestressed cantilever beam. A simple FEM model is implemented to generate the data set required to train the NN. The steps done to construct the FEM are discussed and the FEM model results are verified. The effect of the preload position on the natural frequencies of the beam is studied. A NN is then implemented to predict the natural frequencies of the beam for different beam cross-section geometries and different preload magnitudes and positions. The NN architecture, data processing and training methodology are explained. The NN and FEM results are compared to show the accuracy of the NN predictions. The results are shown to be in good agreement.