Crack identification in magnetoelectroelastic materials using neural networks, self-organizing algorithms and boundary element method
A hybrid approach using AI tools is proposed for crack identification in magnetoelectroelastic materials. A DBEM formulation is used to obtain the training set of a NN. The training set is separated into small training sets using only intrinsic properties of the data set, using the self-organizing algorithms. Extended displacements taken at a specific boundaries are sufficient to provide good damage identification. Noise sensitivity in the data set was reduced to a minimum using Gaussian mixtures algorithm as automated partitioning method.
neural networks self-organizing algorithms damage identification inverse problems smart materials