AUTHOR=Zeng Xianjun , Hu Ning , Liu Yaolu , He Jiawei , Shang Xiaozhuang , Ning Huiming , Shao Lei 

TITLE=Prediction and evaluation of projectile damage in composite plates using the neural network–cloud model

JOURNAL=Frontiers in Materials

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2023.1164090

DOI=10.3389/fmats.2023.1164090

ISSN=2296-8016

ABSTRACT=Composite plates are widely used in aircraft manufacturing industry. The projectile damage of composite plates is affected by complex factors such as material, structure, impact velocity, impact angle. A reliable method is needed for an efficient structural health monitoring. In this paper, a composite plates damage prediction and evaluation model based on cloud model and neural network is proposed, the five types of experimental characteristics are used as input parameters, and the depth and diameter of the damage area are used as output parameters to train the neural network-cloud model. This method transforms the quantitative data of impact damage of composite plate into qualitative damage grade by introducing cloud model, which makes the damage situation more intuitive. The results show that the accuracy of the prediction model is 97.23 %, the accuracy of evaluation model is 92.41 %, and the comprehensive accuracy of the model is 89.85%. The composite damage prediction model has a good prediction performance.