Influences of the mesh in the CAE simulation for plastic injection molding

Abstract

Although computer-aided engineering (CAE) software has been used for many years in the plastic industry, identifying the most appropriate mesh geometry and density remains a challenge. It can affect the accuracy of the simulation, the time and the costs. The evaluation of the most suitable mesh is not easy because the difficulties to obtain the real the values of the pressure and temperature inside the mold. The current work investigates this issue. A mold was manufactured and sensors were installed in its interior. CAE simulations using different mesh geometries and densities were evaluated against the experimental data. The results showed that the computational time was mostly influenced by the mesh geometry. The use of 2D mesh and lower density can lead to a faster and more precise simulation of pressure inside the mold and 3D mesh with lower density can provide a faster and precise simulation of the temperature.

Finite Element-Based Performance Analysis of Encased Composite Columns under Monotonic Axial Compression Load

“…Te mesh size is one of the factors contributing to the convergence criteria. It has been also reported that mesh fneness and coarseness have a signifcant efect on computation time [58,[63][64][65][66]. Te guideline for maximum mesh size was already stated in the previous report [58,67].…”

A new approach to dynamic forecasting of cavity pressure and temperature throughout the injection molding process

“…Today, numerical simulations by computer‐aided engineering software (CAE) are the most used tool to simulate this injection molding process. The investigation presented by Marin et al [ 4 ] demonstrated that current CAE software can achieve high accuracy for predicting temperature and pressure inside mold cavities during injection processes (98% and 97%, respectively), but it takes into account the process dynamics only within one injection cycle. Thus, transient regimes due to thermal perturbations or variations in the polymer properties cannot be predicted by CAE approaches.…”