NDT identification of a crack using ANNs with stochastic gradient descent

Nondestructive testing (NDT) is used to identify the anomalies and defects in inaccessible locations. Various techniques of optimization are used in NDT. In this work, the Artificial Neural Networks (ANNs) are applied with NDT to identify a crack in a conducting medium. In general, deterministic techniques are used with the back propagation algorithm (BP) to train the neural networks. The ANNs which are trained by a deterministic method have a tendency to get trapped in local minima. In this paper a stochastic version of the gradient descent is applied to train the ANNs and it overcomes the difficulties of local minima caused by the sinusoidal fields. The stochastic version used in this approach is based on the Metropolis algorithm which is frequently used in the simulated annealing.