Injection molding optimization with weld line design constraint using distributed multi-population genetic algorithm

Abstract

Weld lines not only detract from an injection-molded part’s surface quality, but also significantly reduce its mechanical strength. It is not always easy to completely eliminate weld lines by simply adjusting the relevant injection mold design or the molding conditions. One solution is to prevent the weld lines from forming in regions that are structurally or aesthetically sensitive. The influence of weld lines on the quality of injection-molded parts cannot be overlooked and weld lines should be regarded as an important design constraint, especially for parts with aesthetic concerns. Since precisely predicting the number of weld lines and their positions and lengths is difficult without executing simulation routines, especially when part geometry is considered a design variable, this study adopts an enhanced genetic algorithm, referred to as distributed multi-population genetic algorithm (DMPGA), combining an optimization algorithm and commercial MoldFlow software with a dominance-based constraint-handling technique and a master–slave distributed architecture. MoldFlow obtains relevant data regarding warpage and weld lines and evaluates the corresponding designs. The dominance-based constraint-handling technique handles the weld line design constraint without needing additional penalty factors. Finally, the master–slave distributed architecture reduces the formidable computational time required for injection molding optimization. To illustrate the high viability of DMPGA, this study provides an outer frame of a digital photo frame as an optimization example. The results of this study show that DMPGA cannot only effectively decrease maximum part warpage without violating the weld line constraint, but also conquer hurdles attributed to constraint handling and computational demand.