A deformation detection method for aircraft skin on uniform pressure by using speckle image correlation technology

Ji, Yue; Li, Zhan; Liu, Liping; Wang, Jingjing; Wu, Jun

doi:10.1016/j.measurement.2020.107525

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Deformation analysis is very important to the whole aircraft manufacturing process, so the research on aircraft deformation has also been paid special attention [16][17][18]. Wang et al [19] based on the static mechanics equation of elastic beam, derived the assembly deformation prediction model, it lays a foundation for the prediction of aircraft deformation deviation analysis.…”

Section: Aircraft Deformation Analysismentioning

confidence: 99%

“…In the process of civil aircraft manufacturing and assembly, the requirements for assembly accuracy are very strict [1][2][3], but in the actual assembly process, because the aircraft structural material is a flexible material [4,5], the aircraft will be deformed when it is assembled in different environments or squeezed by external forces [17]. The deformation of aircraft structure will have a serious impact on the assembly acceptance and actual use, so it is very important to find the deformation of aircraft in time during the assembly process.…”

Section: Introductionmentioning

confidence: 99%

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MMPD-Net: An Aircraft Deformation Prediction Network Based on Multimodal Fusion

Kong

Mao

Wang

et al. 2022

Preprint

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In the assembly process of aircraft manufacturing, the aircraft structural is flexible, it is easy to deform under the influence of material factors and working condition factors. Therefore, it is necessary to propose a deformation prediction model to timely find deformation problems. 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, and proposes an aircraft deformation prediction network that based on multimodal fusion (MMPD-Net). We consider both the aircraft structure data and the working condition data on aircraft deformation prediction. MMPD-Net extracts the features of aircraft structure mode and working condition mode respectively. We propose a multimodal fusion network to fuse features, which include the algorithm of average pooling and Bayesian decision. Compared with the mainstream point cloud classification network, MMPD-Net achieves higher classification accuracy on aircraft deformation dataset.

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Section: Aircraft Deformation Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

MMPD-Net: An Aircraft Deformation Prediction Network Based on Multimodal Fusion

Kong

Mao

Wang

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In recent years, point-cloud-based 3D detection techniques have become increasingly prevalent in the field of aircraft engine and airplane manufacturing. These techniques have been applied to various applications, such as measuring gaps between aircraft engine pipelines [ 1 ], assessing seam measurements on aircraft surfaces [ 2 ], detecting surface deformations [ 3 , 4 ], and identifying rivets [ 5 , 6 ]. By leveraging 3D point cloud data, these techniques enable highly precise and quantitative measurements of object geometries.…”

Section: Introductionmentioning

confidence: 99%

Robust Point Cloud Registration for Aircraft Engine Pipeline Systems

Liu,

Wang,

Huang

et al. 2024

Sensors

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Aircraft engine systems are composed of numerous pipelines. It is crucial to regularly inspect these pipelines to detect any damages or failures that could potentially lead to serious accidents. The inspection process typically involves capturing complete 3D point clouds of the pipelines using 3D scanning techniques from multiple viewpoints. To obtain a complete and accurate representation of the aircraft pipeline system, it is necessary to register and align the individual point clouds acquired from different views. However, the structures of aircraft pipelines often appear similar from different viewpoints, and the scanning process is prone to occlusions, resulting in incomplete point cloud data. The occlusions pose a challenge for existing registration methods, as they can lead to missing or wrong correspondences. To this end, we present a novel registration framework specifically designed for aircraft pipeline scenes. The proposed framework consists of two main steps. First, we extract the point feature structure of the pipeline axis by leveraging the cylindrical characteristics observed between adjacent blocks. Then, we design a new 3D descriptor called PL-PPFs (Point Line–Point Pair Features), which combines information from both the pipeline features and the engine assembly line features within the aircraft pipeline point cloud. By incorporating these relevant features, our descriptor enables accurate identification of the structure of the engine’s piping system. Experimental results demonstrate the effectiveness of our approach on aircraft engine pipeline point cloud data.

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