Warpage Optimization in Injection Molding Using MPSO-HNN and MPSO Algorithm

In this paper, a Hybrid Neural Network trained by Modified Particle Swarm Optimization (MPSO-HNN) is first proposed for predicting warpage. Then the minimum warpage problem is solved by MPSO-HNN coupled with MPSO algorithm based on process parameters in injection molding. A model based on Taguchi experiment is designed to find process parameters' optimum levels by Signal-to-Noise (SN) ratio. All results show that proposed algorithm needs less calculating amounts and spends less time and has better generalization ability than those in related literature. Meanwhile, the method of combining MPSO-HNN with MPSO is suitable to optimization warpage compared with the Taguchi method.