Large‐Area Piezoresistive Tactile Sensor Developed by Training a Super‐Simple Single‐Layer Carbon Nanotube‐Dispersed Polydimethylsiloxane Pad

Advanced Intelligent Systems

Kee Yang Lee¹  Eun Jeong Park¹  Joon Seok Kyeong¹  Kee-Sun Sohn²  Chaewon Park²  Byung Do Lee²  Min-Young Cho²  Jin-Woong Lee² 

[1] Materials Research SectorHyundai Mobis Co.Ltd Yongin-si Gyeonggi-do 16891 South Korea;Nanotechnology & Advanced Materials Engineering Sejong University 209 Neungdong-ro Gwangjin-gu Seoul 143-747 South Korea;

关键词: carbon nanotubes;    deep learning;    polydimethylsiloxane;    piezoresistive materials;    tactile sensing;   

DOI  :  10.1002/aisy.202100123

来源: DOAJ

【 摘 要 】

The revolutionary concept of creating a large‐area tactile sensor by training a simple, bulky material through deep learning (DL) is proposed. This enables the replacement of the conventional tactile sensor comprising a patterned array structure with a super‐simple, single‐layer, large‐area tactile sensor pad. A crude carbon nanotube‐dispersed polydimethylsiloxane pad—with a bias applied to the center and the resultant piezoresistive current detected at several electrodes located on the pad edge—plays a smart sensory role without the need for complicated fabrication of microengineered structures. The piezoresistive current while recording the indented location and the pressure thereon is measured, and then various DL models (a multimodel arrangement is necessary due to the viscoelasticity of the pad) using the collected data are trained. The proposed concept is realized using a tandem model comprising a combination of algorithms selected from deep neural networks, convolutional neural networks, long short‐term memory networks, and 16 state‐of‐the‐art machine learning algorithms. The hold‐out dataset test accuracy for the indented location identification reaches 98.89%, and the goodness of fit for pressure prediction is evaluated with mean squared error of 2.5 × 10−3 and coefficient of determination of 98.05%.

【 授权许可】

Unknown