A Two-stage Optimization System for the Plastic Injection Molding with Multiple Performance Characteristics

摘要

This study proposes a two-stage optimization system to generate the optimal process parameter settings of multi-quality characteristics in the plastic injection molding (PIM) products. In the first stage, Taguchi orthogonal array was employ to arrange the experimental work and to calculate the S/N ratio to determine the initial process parameter settings. Then, S/N ratio predictor and S/N quality predictor was constructed by employed the back-propagation neural network (BPNN). In addition, S/N ratio predictor was along with simulated annealing (SA) used to search for the first optimal parameter combination in order to reduce the PIM process variance. In the second stage, BPNN quality predictor and particle swarm optimization (PSO) was intended to find the optimal parameter settings for the best quality specification. Results from the experimental work show that the proposed two-stage optimization system can create the best process parameter settings which not only meet the quality specification, but also effectively reduce cost.