A Neural Network Modeling of Stress Behavior in Nonlinear Magnetostrictive Materials

Using accurate magnetostriction simulation models during the various design stages of their related devices can positively contribute to the enhancement of their precision. These models are indispensable to different crucial computational activities such as those dealing with active vibration damping devices and optimum clamping stresses for transformer sheets. In this paper, we present a new contribution for the dynamic hysteresis behavior of magnetostrictive materials. To do this, we have used a neural network to model the relationship between the elongation λ and the magnetization M for different loads σ . The derivative of λ according to M is then calculated numerically and integrated in the Jiles–Atherton model for calculating the hysteresis loops.