Measurement of deformation of aircraft structures made of composite materials with fiber-optic sensors

Sagadeev, A.R.; Vazhdaev, E.V.; Avdonina, N.A.; Zakurdaeva, T.A.; Levina, T.M.

doi:10.1109/icoecs50468.2020.9278463

Cited by 6 publications

(4 citation statements)

References 15 publications

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“…Assuming that there are three common points between the two coordinate systems, the above equation can be expressed as (8).…”

Section: Automated Measurement Based On Digital Models Of Aircraftmentioning

confidence: 99%

“…On the one hand, due to the complex structure of most aircraft fuselages and the large number of measurement points throughout the fuselage, the workload is very high [5,6]. On the other hand, the residual deformation measurement of aircraft structure requires high timeliness, and the conventional single-station relay measurement mode is time-consuming and cannot meet the timeliness requirement [7,8].…”

Section: Introductionmentioning

confidence: 99%

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A rapid measurement method for residual deformation of aircraft structures

Guo

Wang

Fang

2023

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023)

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Residual fuselage deformation measurement is vital for comprehensive evaluation of aircraft structural strength and loads to ensure flight safety. However, the existing measurement methods have problems such as low automation and long time spent on data interpretation. This paper proposes a digital rapid measurement method for residual deformation of aircraft structures, which can quickly generate a measurement network construction scheme through digital station deployment and virtual simulation. Further, the automatic measurement and real-time data processing based on the remote control technology. The results show that the method can effectively assist the field decision and significantly improve the efficiency of flight test.

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“…Assuming that there are three common points between the two coordinate systems, the above equation can be expressed as (8).…”

Section: Automated Measurement Based On Digital Models Of Aircraftmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A rapid measurement method for residual deformation of aircraft structures

Guo

Wang

Fang

2023

International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023)

View full text Add to dashboard Cite

show abstract

“…Stone and Krishnamurthy 144 proposed a thrust force controller using an ANN to reduce the development of delaminations caused by the entry and removal of a drill bit to an FRP. Machine learning was employed to classify the Visual inspection [85][86][87][88][89][90] Ultrasonic inspection [91][92][93][94] Eddy current [95][96][97][98][99] Radiography 100-105 (e.g., x-ray) Thermography 97,[106][107][108][109][110] Acoustic emission [111][112][113][114][115] Fiber optic sensors [116][117][118][119][120][121] (e.g., fiber Bragg grating) Piezoelectric transducers [122][123][124][125] Laser vibrometry [126][127][128][129][130][131][132] failure methods of composite plates bolted together. 145 A beneficial application of ML is prediction making.…”

Section: Composite Applications With Machine Learningmentioning

confidence: 99%

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

Nelon

Myers

Hall

2022

Journal of Composite Materials

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Machine learning (ML) has emerged as a useful predictive tool based on mathematical and statistical relationships for various engineering problems. The pairing of structural health monitoring (SHM) and nondestructive evaluation (NDE) methods with ML algorithms has yielded beneficial results in addressing the damage state of a material or system. Damage state descriptions addressed with ML include detecting a damage mechanism, locating a mechanism, identifying the type of mechanism, assessing the extent of the damage mechanism, and estimating the useful remaining life of a material or system. Damage evaluation research of composite materials has progressed with the increased usage of composite structural elements in the aerospace industry. NDE methods are a viable candidate for pairing with ML algorithms to improve damage state monitoring of composite materials due to the complexity associated with the structure of composites. Fiber-reinforced polymers (FRP), for example, contain at least two constituent materials a fiber and matrix material whose mechanical behavior and interactions contribute to the performance of an FRP. Unlike conventional composite analytical models that require explicit information about the constituents and microstructure of a laminate, an ML algorithm can construct damage evaluation predictions when employing exclusively past operational performance or data from an SHM or NDE method. A researcher determines the type of data selected when applying an ML model for trend analysis, anomaly detection, or prediction making. However, no one specific input feature is required for utilizing an ML model, and examples of possible data features include material properties, physical dimensions, and collected evaluation data. In the present review, applications of ML combined with the damage state evaluation of composite materials, particularly examining FRPs, are discussed to demonstrate the predictive capabilities of ML and its viability for future applications, especially in industrial environments, to minimize costs and improve damage detection rates.

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“…Dvurecenska et al [22] use image decomposition analyzed the feature vectors of different aircraft deformations, and evaluated the relationship between the curvature of deformations. Sagadeev et al [23] proposed a method for monitoring the deformation of aircraft structure using optical fiber sensing and measurement technology.…”

Section: Aircraft Deformation Analysismentioning

confidence: 99%

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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Measurement of deformation of aircraft structures made of composite materials with fiber-optic sensors

Cited by 6 publications

References 15 publications

A rapid measurement method for residual deformation of aircraft structures

A rapid measurement method for residual deformation of aircraft structures

The intersection of damage evaluation of fiber-reinforced composite materials with machine learning: A review

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

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