Data Assimilation-Based State Estimation of Composites during Molding

Abstract

Monitoring methods used to observe the molding process of carbon fiber-reinforced plastic (CFRP) facilitate the development of high-quality products. However, current monitoring methods provide only localized state information, and estimating the state of the entire material is difficult. This study proposes a method for estimating the overall state of CFRP during molding based on data assimilation, which integrates theoretical and experimental values. In this method, three types of specimens with different thermal conductivities were considered, and the temperatures throughout each specimen during molding were estimated by data assimilation of surface temperatures measured using thermocouples. To validate the accuracy of this method, the temperatures estimated by data assimilation were compared with those obtained using numerical simulations without data assimilation. The experimental results of all the specimens showed that data assimilation can be used to accurately estimate the surface temperature. The proposed method is therefore effective for monitoring composite molding processes.