FOSS-Based Method for Thin-Walled Structure Deformation Perception and Shape Reconstruction

To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation c...

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Bibliographic Details
Published in:Micromachines (Basel) 2023-03, Vol.14 (4), p.794
Main Authors: Wu, Huifeng, Dong, Rui, Xu, Qiwei, Liu, Zheng, Liang, Lei
Format: Article
Language:English
Subjects:
Accuracy
Aircraft
Algorithms
Analysis
BP neural network
Data analysis
Deformation
Equipment and supplies
Error analysis
Fiber optics
fiber-optic sensor system
Finite element method
Helicopters
Machine learning
Methods
Model testing
Neural networks
one-class SVM
Outliers (statistics)
Perception
Reconstruction
Sensors
shape reconfiguration
Simulation
Strain measurement
Support vector machines
Thin wall structures
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Summary:To improve the accuracy of deformation perception and shape reconstruction of flexible thin-walled structures, this paper proposes a method based on the combination of FOSS (fiber optic sensor system) and machine learning. In this method, the sample collection of strain measurement and deformation change at each measuring point of the flexible thin-walled structure was completed by ANSYS finite element analysis. The outliers were removed by the OCSVM (one-class support vector machine) model, and the unique mapping relationship between the strain value and the deformation variables (three directions of x-, y-, and z-axis) at each point was completed by a neural-network model. The test results show that the maximum error of the measuring point in the direction of the three coordinate axes: the x-axis is 2.01%, the y-axis is 29.49%, and the z-axis is 15.52%. The error of the coordinates in the y and z directions was large, and the deformation variables were small, the reconstructed shape had good consistency with the deformation state of the specimen under the existing test environment. This method provides a new idea with high accuracy for real-time monitoring and shape reconstruction of flexible thin-walled structures such as wings, helicopter blades, and solar panels.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi14040794