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A Reflective Multimode Fiber Vector Bending Sensor Based on Specklegram

OPTICS AND LASER TECHNOLOGY(2024)

Tianjin Univ

Cited 4|Views33
Abstract
The vector bending sensor that senses bend orientation and curvature is desperately needed in the smart medical field. However, the existing fiber-optic vector bending sensor is challenging to detect the 360 degrees bending orien-tation, and both sides of the transmission structure need related equipment resulting in limited application. Here, a reflective multimode fiber vector bending (RMVB) sensor based on the specklegrams is proposed to overcome these challenges. Light source coupling and specklegrams capturing at the same side are achieved by the beamsplitter prism at the proximal end and the reflective coating at the distal end. Specklegrams can be used to estimate the bending state since the bending alters the mode interference. The bending state prediction network is trained using a dataset of 360 degrees bending orientations and 0-5.7 m-1 curvatures. The bending orientation and curvature interval are predicted by the classification model with accuracies of 0.99 and 0.97, respectively. With the mean absolute error lower than 4 % of the measurement range, the regression model can accurately predict the bending orientation and curvature. Therefore, the RMVB sensor with a novel structure capable of curvature sensing in 360 degrees bending orientation provides a promising approach for a single-fiber vector bending sensor and is expected for application in space-tight applications such as interventional operation.
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Key words
Deep learning,Multimode fiber,Specklegrams,Vector bending sensor
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要点】:本文提出了基于散斑图的反射多模光纤矢量弯曲传感器,能够准确探测360°弯曲方向和曲率,具有广阔的应用前景。

方法】:利用分束棱镜实现光源耦合和散斑图捕捉,反射镀膜实现同侧获取散斑图,利用模式干涉来估计弯曲状态,并训练弯曲状态预测网络。

实验】:使用360°弯曲方向和0-5.7 m-1曲率的数据集训练分类模型,弯曲方向和曲率的预测准确率分别为0.99和0.97,回归模型的平均绝对误差低于测量范围的4%。