학술논문

3-D Printed Graphene-Based Piezoresistive Foam Mat for Pressure Detection Through Electrical Resistance Tomography and Machine Learning Classification Techniques
Document Type
Periodical
Source
IEEE Sensors Letters IEEE Sens. Lett. Sensors Letters, IEEE. 7(9):1-4 Sep, 2023
Subject
Components, Circuits, Devices and Systems
Robotics and Control Systems
Communication, Networking and Broadcast Technologies
Signal Processing and Analysis
Sensors
Piezoresistance
Frequency modulation
Tomography
Three-dimensional displays
Piezoelectric transducers
Machine learning
Mechanical sensors
pressure sensors
3-D printing
ecoflex
electrical resistance tomography (ERT)
graphene
machine learning (ML)
nanoplatelets
piezoresistive mat
polymeric foam
water soluble template
Language
ISSN
2475-1472
Abstract
This letter presents the development and investigation of a novel soft piezoresistive foam mat sensor (FMS) based on graphene nanoplatelets (GNPs) for pressure sensing. The sensor is fabricated using a 3-D printed polyvinyl alcohol (PVA) water-soluble sacrificial template, which is then infiltrated with Ecoflex polymer and dip-coated in a GNP-ethanol solution. The mechanical response as well as the high piezoresistive sensitivity (0.31 kPa −1 @ 8 kPa) of the fabricated foam is assessed experimentally. Pressure detection is achieved through electrical resistance tomography (ERT) using the opposite current injection method, and the collected data are processed using machine learning (ML) classification techniques to localize pressure application on the FMS's surface. The experimental results demonstrate the potential of the suggested approach to effectively detect pressure across extensive surface areas, achieving an accuracy of approximately 87.5% or 83.7%, respectively, for identifying the presence of deformation resulting from a single fingertip touch or from the simultaneous touch of two fingers at separate points on different zones of the FMS.