融合
卷积神经网络
微观结构
特征(语言学)
多层感知器
情态动词
材料性能
计算机科学
组分(热力学)
材料科学
人工神经网络
人工智能
模式识别(心理学)
复合材料
物理
哲学
热力学
语言学
作者
Lixin Song,Donglei Wang,Xuwang Liu,Aijun Yin,Zhendong Long
标识
DOI:10.1016/j.sna.2023.114433
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI