@article{et al._2019, title={On the Question of Computer Simulation of Injection Molding Process of the Car Front Bracket Part with the Mesh Sensitivity Analysis}, volume={12}, url={http://sersc.org/journals/index.php/IJCA/article/view/2069}, abstractNote={<p><em>Our</em><em> study describes the influence of mesh model types and the size of their elements on the results of the simulation of injection molding process. </em></p> <p><em>Although computer-aided engineering software is already widely used in the automotive industry, finding the most suitable geometry and grid density in the polymer injection molding simulation remains an unresolved challenge. It can adversely affect simulation accuracy, time and cost. Identifying the most suitable mesh is not easy, as it is difficult to obtain the real values of pressure and temperature inside the mold. Our</em> <em>work</em> <em>explores</em> <em>this</em> <em>problem</em><em>.</em><em> We investigated three methods of generating mesh models (Mid Plane, Dual-Domain, 3D). These three options have been analyzed in CAE program. As a result, recommendations for rapid and effective analysis of the injection molding process are presented. The paper also presents an analysis of the mesh convergence according to each method of constructing mesh models, which allowed us to generate the optimal number of elements in the mesh model to obtain qualitative and reliable results. The </em><em>modeling was carried out with the use of various mesh geometries. As it turned out, 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, while the 3D mesh with lower density can provide a faster and precise simulation of the temperature.</em></p> <p><em>The results can be used to correct the casting process, as well as to prevent defects formed during injection molding of plastics.</em></p&gt;}, number={6}, journal={International Journal of Control and Automation}, author={et al., Aleksei Maksimov}, year={2019}, month={Dec.}, pages={211 - 222} }