Optimization of moldflow molding process with taguchi in automotive plastic part production

Injection molding is widely used in the automotive industry due to its advantages such as lightweight design, cost-effectiveness, and design flexibility. However, undesirable dimensional deformations such as warpage and volumetric shrinkage can significantly affect the geometric accuracy of plastic parts. These defects negatively impact part functionality and assembly compatibility. At this point, computer-aided simulation tools like Moldflow play a critical role in detecting such issues early in the design stage, thus reducing production errors and increasing efficiency. Moldflow allows virtual evaluation of the injection process and provides process optimization, resulting in time and cost savings. Additionally, due to the large number of influencing parameters, the Taguchi design of experiments method is frequently preferred for determining the optimum parameter set with a minimal number of experiments. This thesis aims to enhance the dimensional stability of outer trim components used in automotive lighting systems by optimizing both material selection and process parameters. The study consists of two stages. In the first stage, Moldflow simulations were conducted on 15 different polypropylene (PP) composites formulated with varying types of fillers (talc, glass fiber, carbon fiber, calcium carbonate, wollastonite) and reinforcement ratios (10%, 20%, 30%). The obtained warpage and volumetric shrinkage values were evaluated based on the Taguchi L16 orthogonal array, and the S/N (Signal-to-Noise) ratios were calculated according to the "smaller-is-better" criterion. As a result of this analysis, the PP composite with 30% talc exhibited the most balanced and dimensionally stable performance among all configurations. In the second stage, following the identification of 30% talc-filled PP as the most suitable material, optimization of the injection molding process parameters was performed. Four factors (melt temperature, mold temperature, injection time, and cooling time), each at three levels, were designed using the Taguchi L27 orthogonal array. Moldflow simulations were carried out for all experimental runs, and the resulting warpage and shrinkage values were statistically analyzed through average S/N ratios. According to the findings, Experiment 18 (melt temp: 220 °C, mold temp: 50 °C, injection time: 4 s, cooling time: 40 s) yielded the best overall performance. This condition resulted in a minimum warpage value of 0.25 mm and an acceptable shrinkage level of 0.73%. Furthermore, it achieved the highest average S/N ratio of +7,3 among all trials. The Moldflow visual outputs of the optimum condition confirmed homogeneous material distribution, symmetrical warpage vectors, and minimum internal stress accumulation. These results are consistent with findings in the literature, emphasizing the importance of balanced cooling and suitable thermal conditions during molding. Moreover, the entire study was conducted virtually, without the need for physical prototyping, providing substantial time and cost advantages for industrial applications. In conclusion, this thesis presents an integrated and systematic optimization framework combining material selection and process parameter tuning. The proposed methodology fills a gap in the literature by addressing multiple variables simultaneously using Moldflow simulation and Taguchi analysis. It offers a repeatable and scalable model for future applications in the field of plastic part manufacturing, particularly in the automotive industry.