基于多模态和图卷积的飞机部段形变预测方法

Prediction method of aircraft segment deformation based on multi-mode and graph convolution

近年来,随着人工智能技术的发展,深度神经网络在智能制造中得到了广泛应用。本文将深度神经网络与飞机部段的形变预测相结合,提出了一种基于图卷积和多模态的飞机部段形变预测方法。在对飞机部段的形变分析中,对飞机部段的结构数据与工况数据两种模态的数据进行特征提取并在特征级和决策级进行融合。飞机结构数据为点云数据,具有非欧几里得数据的特性,在对飞机部段结构数据进行特征提取时,引入了基于图卷积的特征提取网络。基于ModelNet40和飞机部段的结构和工况数据构建了包含4种飞机部段的形变数据集,并在该数据集上进行了实验。实验结果表明,该方法在各个部段中的平均预测均方误差为0.188,并在机头部段取得了最好的预测结果,可以有效的对飞机部段的形变状况进行预测。

In recent years,with the development of artificial intelligence technology,deep neural network has been widely used in intelligent manufacturing.This paper combines deep neural network with aircraft deformation prediction,proposes a prediction method of aircraft segment deformation based on graph convolution and multi-mode.In the deformation analysis of aircraft segment,the model extracts the features of aircraft structure mode and working condition mode respectively,and fusion at the decision-making level.When extract features from aircraft segment structure data,the aircraft structure data is in point cloud format and has the characteristics of non-Euclidean data,this paper introduce the graph convolution.Based on ModelNet40 and real aircraft segment working condition data,construct aircraft segment deformation dataset deformation dataset including four aircraft segments,and experiments are conducted on this dataset.The experimental results show that the prediction mean square error of this method is 0.188,and get the best prediction in the nose segment of the aircraft,which can effectively predict the deformation of aircraft segments.