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

Zhihao Kong; Jianhua Mao; Xiaofeng Lu

doi:10.1117/12.2681547

1 June 2023 MMDP-Net: an aircraft deformation prediction network based on feature fusion

Zhihao Kong, Jianhua Mao, Xiaofeng Lu

Proceedings Volume 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023); 127182J (2023) https://doi.org/10.1117/12.2681547
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 2023, Nanjing, China

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 decision-making algorithms. On the aircraft deformation dataset, MMDP-Net gets greater categorization accuracy than the standard point cloud classification network.

Citation Download Citation

Zhihao Kong, Jianhua Mao, and Xiaofeng Lu "MMDP-Net: an aircraft deformation prediction network based on feature fusion", Proc. SPIE 12718, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2023), 127182J (1 June 2023); https://doi.org/10.1117/12.2681547

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KEYWORDS

Deformation

Point clouds

Feature extraction

Feature fusion

Data modeling

Aircraft structures

Distortion

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