Prediction of mechanical properties of composite materials using multimodal fusion learning

Efficient and accurate measurement of material mechanical properties is important for material development. The mechanical properties of materials are comprehensively affected by factors such as material composition and microstructure. The existing single-mode prediction methods have the problems of poor mapping integrity and low prediction accuracy. In this paper, a multimodal fusion prediction model integrating material microstructure information and material composition information is proposed. Firstly, Convolutional neural network(CNN) is used to extract the material microstructure features, and Multilayer perceptron(MLP) is used to extract the component features. Then, the adaptive feature vector fusion module designed to adjust the influence of different modal information on the mechanical properties is used to achieve high-level feature fusion, and neural network is used to complete the mechanical properties prediction. The method is verified on the data of an alternative composite material. The method proposed provides a promising solution for the measurement and prediction of material properties.