Using Multiobjective Evolutionary Algorithms in the Optimization of Operating Conditions of Polymer Injection Molding

摘要

A Multiobjective Optimization Genetic Algorithm, denoted as Reduced Pareto Set Genetic Algorithm with Elitism (RPSGAe), has been applied to the optimization of the polymer injection molding process. The aim is to implement an automatic optimization scheme capable of defining the values of important process operating conditions (such as melt and mould temperatures, injection time, and holding pressure), yielding the best performance in terms of prescribed criteria (such as temperature difference on the molding at the end of filling, the maximum cavity pressure, the pressure work, the volumetric shrinkage and the cycle time). The methodology proposed was applied to some case studies. The results produced have physical meaning and correspond to a successful process optimization.