Injection Molding Parameter Optimization Using Taguchi and Moldflow
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Geopolitical tensions in the Middle East have increased uncertainty in petrochemical and polypropylene supply, reducing plastic raw material availability and forcing plastic injection companies to improve process efficiency, minimize scrap, and reduce production losses. In automotive component manufacturing, the Cover Defroster product still faces short shot defects caused by incomplete molten plastic flow into the mold cavity. Many injection molding companies also still rely on trial-and-error machine settings, which increases material waste, setting time, and production cost. This study aims to optimize injection molding parameters using the Taguchi method and Moldflow simulation to eliminate short shot defects and minimize cycle time. This research used a computer simulation-based experimental design. The product studied was a polypropylene type LA880T. Four process parameters were tested using an Orthogonal Array L16, namely injection pressure, mold temperature, melt temperature, and injection time. Moldflow Insight was used to simulate material flow, while ANOVA, Tukey test, and Signal-to-Noise Ratio with the Smaller-the-Better approach were used to analyze cycle time performance. The simulation showed that trials 1 to 4 produced short shot defects and were rejected. ANOVA results showed that injection time had a significant effect on cycle time, while injection pressure, mold temperature, and melt temperature had no significant effect. The best parameter combinations were trials 6, 12, and 15, which produced good products without short shot defects and achieved the shortest cycle time of 24.12 seconds. The integration of Taguchi and Moldflow effectively supports parameter optimization, reduces defect risk, and improves injection molding process efficiency.
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