Magnetostrictive tactile sensor for texture detection

Bo-wen WANG; Xiao-dong WANG; Yun-kai LI; Li-li WAN; Wen-dong ZHENG; Jia-qi WEI

doi:10.3788/OPE.20182612.2991

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Magnetostrictive tactile sensor for texture detection

Micro/Nano Technology and Fine Mechanics | 更新时间：2020-07-05

- Magnetostrictive tactile sensor for texture detection
- Optics and Precision Engineering Vol. 26, Issue 12, Pages: 2991-2997(2018)
- 作者机构：
  
  1.河北工业大学省部共建电工装备可靠性与智能化国家重点实验室, 天津 300130
  2.河北工业大学河北省电磁场与电器可靠性重点实验室, 天津 300130
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20182612.2991
  CLC： TP212.1
- Received：19 January 2018，
  
  Accepted：02 March 2018，
  
  Published：25 December 2018
- 稿件说明：
移动端阅览
Bo-wen WANG, Xiao-dong WANG, Yun-kai LI, et al. Magnetostrictive tactile sensor for texture detection[J]. Optics and precision engineering, 2018, 26(12): 2991-2997.
DOI：

Bo-wen WANG, Xiao-dong WANG, Yun-kai LI, et al. Magnetostrictive tactile sensor for texture detection[J]. Optics and precision engineering, 2018, 26(12): 2991-2997. DOI： 10.3788/OPE.20182612.2991.

摘要

为了实现对不同物体表面微观结构的检测，并判断出不同物体的粗糙度和细密度，利用铁镓合金材料（Galfenol）的逆磁致伸缩效应设计并制作了一种高精度和高响应的纹理探测触觉传感器。基于欧拉-伯努利梁结构动力学理论、磁致伸缩材料线性本构方程和法拉第电磁感应定律建立了纹理表面微观结构与输出电压之间的关系。实验结果表明：在粗糙度大于6.5的范围内，传感器可以精确识别物体的粗糙度；在细密度大于6的范围内，提取谐波频率的方法对细密度的识别具有较高的灵敏度；在细密度小于6的范围内，提取功率谱重心的方法对细密度的识别具有较高的灵敏度。因此，利用传感器获得的信号，通过特征值提取可以表征物体的粗糙-光滑、稀疏-细密属性。

Abstract

Texture is the embodiment of the distribution of surface microstructure. Tactile texture is a crucial factor to consider for improvement of the perception and exerting control over the environment of the material. In this study

a highly accurate and responsive tactile sensor was designed and fabricated using the inverse magnetostrictive effect of Galfenol to detect the surface microstructure of different objects and determine their roughness and fine density. Based on the Euler-Bernoulli beam dynamics theory

linear constitutive equations of magnetostrictive materials

and Faraday's law of electromagnetic induction

a relationship was established between the microstructure of the textured surface and the output voltage. The experimental results showed that the sensor had high sensitivity to object roughness recognition for roughness greater than 6.5. For fineness greater than 6

the method of extracting harmonic frequency was highly sensitive in identifying fineness. However

when the fineness was less than 6

the method of extracting the center of gravity of the power spectrum had high sensitivity for the identification of fineness. These results showed that the signal obtained by the sensor could be used to characterize the rough-smooth and sparse-fine attributes of different objects by eigenvalue extraction.

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Related Institution

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