Dynamic optimization of injection molding process variables by evolutionary programming methods

Injection molding is a nonlinear, multivariable process which involves several critical parameter settings for quality product. Determining optimal parameter settings is the challenging task due to the influence of environmental conditions on the resin flow within the injection molding machine. This paper deals with various optimization techniques (differential evolution, DE, genetic algorithm, GA and Particle Swarm Optimization, PSO) which could be adopted to adjust the various process parameters of injection molding in accordance with level of error identified through quality checking.