Weight reduction of aluminum disc wheels under fatigue constraints using a sequential neural network approximation method

Abstract

This paper describes a weight reduction problem of aluminum disc wheels under cornering fatigue constraints. It is a special structural optimization problem because of the existence of the implicit fatigue constraint. A sequential neural network approximation method is presented to solve this type of discrete–variable engineering optimization problems. First a back-propagation neural network is trained to simulate the feasible domain formed by the implicit constraints using just a few training data. A search algorithm then searches for the “optimal point” in the feasible domain simulated by the neural network. This new design point is checked against the true implicit constraints to see whether it is feasible, and the new training data is then added to the training set. This process continues in an iterative manner until we get the same design point repeatedly and no new training point is generated. In each iteration, only one evaluation of the implicit constraints is needed to see whether the current design point is feasible. No precise function value or sensitivity calculation is required.