Abstract: Based on a deeper understanding and a reasonable analysis on the rapid heat cycling molding (RHCM) technology, the process quality index including wrap age, sink index, and volumetric shrinkage were selected as predictive index of plastic part manufactured by RHCM technology. The main process parameters including mold temperature, melt temperature, injection pressure, pressure at v/p switch-over point, and packing pressure were defined as predictive variances as well. An efficient multifactor comprehensive predictive model was created between molding quality predictive index and main process parameters by coupling with CAE analysis, Taguchi method, and multivariable liner regression methodology. Although the dimension theory did not referred to, it was used during the former research. The comprehensive quality predictive model was interfaced with an effective updated GA to find the optimum process parameters values. A case results showed that the model was effective and feasible.

Key words: rapid heat cycling molding, taguchi method, genetic algorithm, process optimization, multifactor comprehensive quality predictive