MMDP-Net: an aircraft deformation prediction network based on feature fusion

Abstract

The structural flexibility of the aircraft makes it simple for it to distort during construction as a result of material and working environment variables. Therefore, a deformation forecast model must be proposed in order to quickly identify deformation issues. Deep neural networks are now frequently used in clever production as a result of the advancement of artificial intelligence technology. In this article, aircraft deformation prediction using deep neural networks is combined with a multimodal fusion-based aircraft deformation prediction network is proposed (MMDP-Net). In order to forecast aircraft deformation, we take into account both the aircraft construction data and the operating state data. The characteristics of the aircraft construction mode and the operating state mode are extracted by MMDP-Net, respectively. To combine characteristics, we suggest a multimodal fusion network that uses the average pooling and Bayesian decisionmaking algorithms. On the aircraft deformation dataset, MMDP-Net gets greater categorization accuracy than the standard point cloud classification network.