IEEE Access (Jan 2021)
Direct and Extended Piezoresistive and Piezoelectric Strain Fusion for a Wide Band PVDF/MWCNT-Based 3D Force Sensor
Abstract
This paper presents a novel 3D force sensor design based on in-situ nanocomposite strain sensors. The polymer matrix of the polyvinylidene fluoride (PVDF) and multi-walled carbon nanotubes (MWCNT) conductive filler nanocomposite film have been chosen as sensing elements for the 3D force sensor. A bioinspired tree branch design was used as the 3D force sensor’s elastic structure, that was built using thin Aluminum plates and a laser cutting fabrication process. The PVDF/MWCNT films contained piezoresistive and piezoelectric characteristics, allowing for static/low and dynamic strain measurements, respectively. Two compositions with 0.1 and 2 wt.% PVDF/MWCNT sensing elements were selected for piezoelectric and piezoresistive strain measurements, respectively. These characteristic measurements were investigated under different loading frequencies in a simply supported beam experiment. The 3D force sensor was tested under dynamic excitation in the Z-direction and the X-direction. A Direct Piezoresistive/Piezoelectric fusion (DPPF) method was developed by fusing the piezoresistive and piezoelectric measurements at a given frequency that overcomes the limited frequency ranges of each of the strain sensor characteristics. The DPPF method is based on a fuzzy inference system (FIS) which is constructed and tuned using the subtractive clustering technique. Different nonlinear Hammerstein-Wiener (nlhw) models were used to estimate the actual strain from piezoresistive and piezoelectric measurements at the 3D force sensor. In addition, an Extended direct Piezoresistive/Piezoelectric fusion (EPPF) algorithm is introduced to enhance the DPPF method via performing the fusion in a range of frequencies instead of a particular one. The DPPF and EPPF methods were implemented on the 3D force sensor data, and the developed fusion algorithms were tested on the new 3D force sensor via experimental data. The simulation results show that the proposed fusion methods have been effective in achieving lower Root Mean Square Error (RMSE) in the estimated strain than those obtained from the tuned nlhw models at different operating frequencies.
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