Estimaion of the buckling loads of helical coil springs with the help of neural networks

This work proposes the use of artificial neural networks (ANN) to predict perfectly the critical buckling loads of cylindrical isotropic helical springs with fixed ends and with circular sections, and also having large pitch angles. The buckling equations of cylindrical isotropic helical springs loaded axially consist of a set of twelve linear differential equations. As finding a solution in an analytical manner is too difficult, they are solved numerically in an exact manner based on the transfer-matrix method to collect consistent dimensionless numerical data for the training process. Then almost perfect weight values are obtained to predict the non-dimensional buckling loads. A good agreement is observed with the data available in the literature.