The effects of plastic injection parameters on dimensional deviation values n the modeling and production of automotive fuse boxes

In this study, the effects of production parameters on the dimensional deviation of an automotive part produced by plastic injection molding method were examined. The main objective of the research is to analyze the effect of melt temperature and packing time on dimensional deviation and to determine the parameter combinations that give the greatest dimensional accuracy. In the study, polypropylene raw material with 20% talc added was used and physical experiments were carried out by applying three different melt temperatures (220°C, 240°C, 260°C) and three different packing times (3 sec, 6 sec, 15 sec). The examined fuse box provides the safety of the electrical system by protecting the fuse modules in vehicles. Measurements were taken on the experiment parts with a 3D scanning device immediately and 24 hours after production. In addition, the plastic injection process was simulated in a computer environment using the Moldflow software. All results obtained were compared with the target part geometry. The experimental results showed that longer packing time decreased the dimensional deviation, while higher melt temperature increased the dimensional deviation. The parameter combination providing the best dimensional accuracy was determined as 220°C melt temperature and 15 seconds packing time. However, in terms of production efficiency, 220°C melt temperature and 3 seconds packing time is a preferable option as it provides higher production quantities. The Moldflow simulation successfully predicted the physical production results in general, but small deviations were observed at certain measurement points. In order to improve the simulation results, more comprehensive inputs are needed to improve the accuracy of the simulation. This study contributes to the optimization of production parameters in plastic injection molding processes. The obtained results are an important resource for reducing production costs, increasing quality and improving production processes in the industry.