Evaluation of Effect of Plastic Injection Molding Process Parameters on Shrinkage Based on Neural Network Simulation

Abstract

The effects of process parameters, mold temperature (T _mo), melt temperature (T _me), cooling time (t_c ), fill pressure (P_f ), packing pressure (P_p ), and packing time (t_p ) on the shrinkage of injection molded polypropylene were investigated by utilizing a combination of the Artificial Neural Network (ANN) method and Moldflow software. An ANN model is developed to understand the relationship between plastic injection molding process parameters and shrinkage. The test results on the performance of the ANN model show that it can predict the shrinkage with reasonable accuracy. The simulation results show that the most important process parameter affecting shrinkage is P_p , followed by T _me and T _mo, with t_c , P_f , and t_p having the least effect. Shrinkage increases with the elevated T _mo and t_c . In contrast, the increases in P_p , T_me , t_p , and P_f cause shrinkage to decrease. The strongest effect on the shrinkage is the amount of material forced into the mold, followed by the crystallinity and orientation of the material.

Keywords:

• neural network
• plastic injection molding
• process parameter
• shrinkage