State Estimation Inside Injection Molds Using Data Assimilation and Computational Fluid Dynamics

Abstract

Injection molding is the most widely used process for manufacturing plastic products. Because the quality of plastic products depends significantly on the process conditions, an accurate understanding of the state inside the mold is crucial. This approach leads to front-loading, reducing losses and stabilizing the quality of molded products. In recent years, the importance of computer simulations for minimizing the number of experimental iterations required for manufacturing and for reducing the overall development costs has increased. However, concerns regarding the accuracy of computer simulations are increasing as well, thus highlighting the necessity to address uncertainties in simulation conditions. Herein, we propose a high-precision state-estimation method using computational fluid dynamics (CFD) and data assimilation to accurately understand the state inside a mold. Computer simulations are initially performed using OpenFOAM. Subsequently, pressure sensors are installed inside the mold to obtain observational data. The simulation results are compared with the observed data. Next, data assimilation is performed to improve the simulation accuracy and investigate the internal state of the mold more accurately. The data-assimilation method employed in this study is the ensemble Kalman filter. We successfully demonstrated the effectiveness of high-precision state estimation using CFD and data assimilation.