针刺 C/C 复合材料结构-工艺-性能一体化软件平台

The carbon fiber placement mode and needle-punching process of needle-punched C/ C composite have an important influence on its mechanical properties. Combined with material information science, a set of structure-process-performance integrated software platform for needle-punched C/ C composites based on Web interface was developed, including two modules: material database and machine learning model. The "structure-process-performance" data from experiments, industrial production or numerical simulation were stored in the developed database module for effective storage and management. By using these data, the machine learning model was trained based on neural network regression algorithm, and the mapping relationship among structure-process-performance was established. Aiming at current expensive and rare experimental data, high-throughput finite element numerical simulation was used to obtain the material parameter data of the structure-process-performance (effective elastic mechanics parameters) of needled C/ C composites, and the machine learning model for predicting mechanical properties was trained and deployed in the software platform. The machine learning model was invoked through the Web user interface to predict the effective elastic mechanical parameters of needled C/ C composites under specific structural and process parameters. The results show that the relative error of the predicted values is less than 2%, which verifies the function and application of the software platform. This software platform is expected to further predict material properties based on the experimental data and corresponding machine learning models, so as to provide theoretical guidance for designing and optimizing the structural parameters and process parameters of needled C/C composites.