This paper focuses on the development of a thin normal-shear force sensor based on the use of a supercapacitive sensing mechanism. The sensor has a quad structure with four sensing units in which the bottom substrate has four pairs of electrodes while the top deformable portion has a solid-state electrolyte. The application of normal and shear forces increases the contact area between the solid-state electrolyte and the electrodes. Key innovations in the sensing mechanism include the use of a paper-based solid state electrolyte, the use of 3d-printing with a combination of soft and hard polymers to construct the top portion of the sensor, and the use of deep learning to model the sensor response. The sensor can accept any combination of shear and normal forces and can measure both their magnitudes and orientation. The deep learning based sensor response model enables accurate sensor calibration while accommodating the imperfections in the stiffness distribution of the sensor. The normal and shear force sensitivities are of the order of 50 nF/N and 22 nF/N respectively, which are orders of magnitude larger than the sensitivity of a traditional capacitive force sensor.
Bibliographical noteFunding Information:
Manuscript received June 3, 2020; accepted July 15, 2020. Date of publication August 4, 2020; date of current version December 4, 2020. This work was supported in part by the National Science Foundation under Grant EFMA 1830958. The associate editor coordinating the review of this article and approving it for publication was Dr. Emiliano Schena. (Corresponding author: Rajesh Rajamani.) Ye Zhang and Rajesh Rajamani are with the Department of Mechanical Engineering, The University of Minnesota at Twin Cities, Minneapolis, MN 55455 USA (e-mail: firstname.lastname@example.org; email@example.com).
- deep learning
- Force sensors
- sensor calibration
- shear and normal force sensors
- supercapacitive sensor