Modelling of Decision Making in the Production of Stator Core Using a GA-ANN Approach

Abstract

In the production of stator cores, it is relied upon experienced engineers to make time sensitive decisions on the number of compensation sheets to be added to achieve uniform pressure distribution though out the laminations. However, this method yields inconsistent results as humans are unable to store and analyze large amounts of data. In this paper ANNs have been employed to help the engineers with the decision making process. The ANNs are trained using a hybrid Genetic Algorithm (GA) – Levenberg-Marquardt (LM) to avoid local convergence. When used on testing data sets, the ANNs displayed a high degree of prediction accuracy indicating their ability to simulate the decision making process of these experienced engineers.