Three-dimensional force sensors based on all-fiber Fabry–Perot strain sensors

He, Xiaoqing; Ran, Zengling; Xiao, Yaqin; Xu, Tiantian; Shen, Feng; Ding, Zhaoyang; He, Zhengxi; Rao, Yunjiang; Zeng, Debiao; Chu, Wangwei; Li, Xiankun; Wei, Yabin

doi:10.1016/j.optcom.2020.126694

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Xiu He proposed a novel three-dimensional force sensor based on an ultrasensitive all-fiber extrinsic Fabry-Perot strain sensor as well as a paperclipshaped elastomer. Experimental results show that all of the independent coefficients are significantly larger than the corresponding interference coefficients; however, crossinterference errors were not quantified [8].…”

Section: Introductionmentioning

confidence: 91%

“…Three-dimensional force (generally expressed as F x , F y , and F z ) refers to the forces along the X, Y, and Z directions in the space Cartesian coordinate system. Three-dimensional force measurement is necessary in many important fields, such as precision manufacturing and robots [1][2][3], medical equipment [4,5], wearable devices [6,7], structural health monitoring of large buildings [8], etc. Three-dimensional force sensor is an important tool for measuring three-dimensional forces.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor

Zhao

2023

Sensors

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Cross-interference is not only an important factor that affects the measuring accuracy of three-dimensional force sensors, but also a technical difficulty in three-dimensional force sensor design. In this paper, a cross-interference suppression method is proposed, based on the octagonal ring’s structural symmetry as well as Wheatstone bridge’s balance principle. Then, three-dimensional force sensors are developed and tested to verify the feasibility of the proposed method. Experimental results show that the proposed method is effective in cross-interference suppression, and the optimal cross-interference error of the developed sensors is 1.03%. By optimizing the positioning error, angle deviation, and bonding process of strain gauges, the cross-interference error of the sensor can be further reduced to −0.36%.

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Section: Introductionmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor

Zhao

2023

Sensors

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“…In detailed related studies, Xiu He utilized three orthogonally distributed optical fiber Fabry-Perot strain sensors to achieve three-dimensional force sensing [ 23 ]. The sensor showed good repeatability and promising application prospects.…”

Section: Introductionmentioning

confidence: 99%

Monolithic integrated optoelectronic chip for vector force detection

Feng,

Wang,

Zhanghu

et al. 2024

Microsyst Nanoeng

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Sensors with a small footprint and real-time detection capabilities are crucial in robotic surgery and smart wearable equipment. Reducing device footprint while maintaining its high performance is a major challenge and a significant limitation to their development. Here, we proposed a monolithic integrated micro-scale sensor, which can be used for vector force detection. This sensor combines an optical source, four photodetectors, and a hemispherical silicone elastomer component on the same sapphire-based AlGaInP wafer. The chip-scale optical coupling is achieved by employing the laser lift-off techniques and the flip-chip bonding to a processed sapphire substrate. This hemispherical structure device can detect normal and shear forces as low as 1 mN within a measurement range of 0–220 mN for normal force and 0–15 mN for shear force. After packaging, the sensor is capable of detecting forces over a broader range, with measurement capabilities extending up to 10 N for normal forces and 0.2 N for shear forces. It has an accuracy of detecting a minimum normal force of 25 mN and a minimum shear force of 20 mN. Furthermore, this sensor has been validated to have a compact footprint of approximately 1.5 mm2, while maintaining high real-time response. We also demonstrate its promising potential by combining this sensor with fine surface texture perception in the fields of compact medical robot interaction and wearable devices.

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“…Researchers have proposed various types of optical fiber stress sensors (Asriani et al, 2020;Cai et al, 2020;Guo et al, 2020;Tan et al, 2020;Zheng et al, 2020;Tang et al, 2021;Xiang, 2021), which are based on single-mode multi-mode single-mode tapered fiber Bragg grating sensors. The measurement range of the sensor is 0-960 με; A Fabry Perot based strain sensor (He et al, 2020). The cavity is formed by splicing a new advanced silicon tube between two standard single-mode fibers.…”

Section: Introductionmentioning

confidence: 99%

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

Qiao

Wang

2022

Front. Energy Res.

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In order to further improve the measurement range and accuracy of optical fiber stress sensor based on the interference between rising vortex beam and plane wave beam, a new stress demodulation model is designed. This model proposes a method to optimize the long-term and short-term memory network (LSTM) model by using sparrow search algorithm (SSA), extract the main characteristics of the influence of various variables on optical fiber stress sensor, and fit the relationship between sensor stress and beam phase difference. This method is an attempt of the deep learning model LSTM in the study of stress mediation model. There are very few related studies, and it is very necessary to fill this gap. In the experiment, the SSA-LSTM neural network is trained by using the data of stress and phase difference measured by the optical fiber stress sensor. The test results show that the mean error of SSA-LSTM neural network is less than that of LSTM neural network, which shows that the combination of SSA-LSTM model and optical fiber stress sensor can make its measurement accuracy higher, The algorithm can more effectively reduce the influence of the surrounding environment and the influence of the light source fluctuation on the measurement range and accuracy of the optical fiber sensor, and has good practical application value. It is proved that the deep learning LSTM neural network has good application value in the light intensity optimization of optical fiber stress sensor.

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Three-dimensional force sensors based on all-fiber Fabry–Perot strain sensors

Cited by 5 publications

References 23 publications

Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor

Research of a Cross-Interference Suppression Method for Piezoresistive Three-Dimensional Force Sensor

Monolithic integrated optoelectronic chip for vector force detection

Light intensity optimization of optical fiber stress sensor based on SSA-LSTM model

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