This study addresses a design and calibration methodology based on numerical finite element method (FEM) modeling for the development of a soft tactile sensor able to simultaneously solve the magnitude and the application location of a normal load exerted onto its surface. The sensor entails the integration of a Bragg grating fiber optic sensor in a Dragon Skin 10 polymer brick (110 mm length, 24 mm width). The soft polymer mediates the transmission of the applied load to the buried fiber Bragg gratings (FBGs), and we also investigated the effect of sensor thickness on receptive field and sensitivity, both with the developed model and experimentally. Force-controlled indentations of the sensor (up to 2.5 N) were carried out through a cylindrical probe applied along the direction of the optical fiber (over an ∼90 mm span in length). A finite element model of the sensor was built and experimentally validated for 1 and 6 mm thicknesses of the soft polymeric encapsulation material, considering that the latter thickness resulted from numerical simulations as leading to optimal cross talk and sensitivity, given the chosen soft material. The FEM model was also used to train a neural network so as to obtain the inverse sensor function. Using four FBG transducers embedded in the 6-mm-thick soft polymer, the proposed machine learning approach managed to accurately detect both load magnitude (R = 0.97) and location (R = 0.99) over the whole experimental range. The proposed system could be used for developing tactile sensors that can be effectively used for a broad range of applications.

中文翻译：

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一种基于机器学习的方法来解决软材料触觉传感器中的接触位置和力。

本研究提出了一种基于数值有限元法 (FEM) 建模的设计和校准方法，用于开发能够同时解决施加在其表面上的法向载荷的大小和应用位置的软触觉传感器。该传感器需要在 Dragon Skin 10 聚合物砖（长 110 毫米，宽 24 毫米）中集成布拉格光栅光纤传感器。软聚合物介导了施加负载到埋入式光纤布拉格光栅 (FBG) 的传输，我们还使用开发的模型和实验研究了传感器厚度对接收场和灵敏度的影响。传感器的力控制压痕（高达 2.5 N）是通过沿光纤方向应用的圆柱形探针（长度跨度约为 90 mm）进行的。建立了传感器的有限元模型，并针对 1 和 6 mm 厚度的软聚合物封装材料进行了实验验证，考虑到后者的厚度是由数值模拟产生的，因为在给定所选软材料的情况下，会导致最佳的串扰和灵敏度。FEM模型也被用来训练一个神经网络以获得逆传感器函数。使用嵌入在 6 毫米厚的软聚合物中的四个 FBG 传感器，所提出的机器学习方法能够准确地检测两个负载大小（FEM模型也被用来训练一个神经网络以获得逆传感器函数。使用嵌入在 6 毫米厚的软聚合物中的四个 FBG 传感器，所提出的机器学习方法能够准确地检测两个负载大小（FEM模型也被用来训练一个神经网络以获得逆传感器函数。使用嵌入在 6 毫米厚的软聚合物中的四个 FBG 传感器，所提出的机器学习方法能够准确地检测两个负载大小（R  = 0.97）和位置（R  = 0.99）在整个实验范围内。所提出的系统可用于开发可有效用于广泛应用的触觉传感器。