Journal of Sensor Science and Technology (센서학회지)

The Korean Sensors Society (한국센서학회)
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Highly Flexible Piezoelectric Tactile Sensor based on PZT/Epoxy Nanocomposite for Texture Recognition

텍스처 인지를 위한 PZT/Epoxy 나노 복합소재 기반 유연 압전 촉각센서

  • Yulim Min (Department of Advanced Device Technology, University of Science and Technology (UST)) ;
  • Yunjeong Kim (Intelligent Components and Sensors Research Section, Electronic, and Telecommunication Research Institute (ETRI)) ;
  • Jeongnam Kim (Department of Advanced Device Technology, University of Science and Technology (UST)) ;
  • Saerom Seo (Intelligent Components and Sensors Research Section, Electronic, and Telecommunication Research Institute (ETRI)) ;
  • Hye Jin Kim (Department of Advanced Device Technology, University of Science and Technology (UST))
  • 민유림 (과학기술연합대학교대학원 ICT 차세대 소자 공학과) ;
  • 김윤정 (한국전자통신연구원 지능형부품센서연구실) ;
  • 김정남 (과학기술연합대학교대학원 ICT 차세대 소자 공학과) ;
  • 서새롬 (한국전자통신연구원 지능형부품센서연구실) ;
  • 김혜진 (과학기술연합대학교대학원 ICT 차세대 소자 공학과)
  • Received : 2023.01.31
  • Accepted : 2023.02.27
  • Published : 2023.03.31
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Abstract

Recently, piezoelectric tactile sensors have garnered considerable attention in the field of texture recognition owing to their high sensitivity and high-frequency detection capability. Despite their remarkable potential, improving their mechanical flexibility to attach to complex surfaces remains challenging. In this study, we present a flexible piezoelectric sensor that can be bent to an extremely small radius of up to 2.5 mm and still maintain good electrical performance. The proposed sensor was fabricated by controlling the thickness that induces internal stress under external deformation. The fabricated piezoelectric sensor exhibited a high sensitivity of 9.3 nA/kPa ranging from 0 to 10 kPa and a wide frequency range of up to 1 kHz. To demonstrate real-time texture recognition by rubbing the surface of an object with our sensor, nine sets of fabric plates were prepared to reflect their material properties and surface roughness. To extract features of the objects from the detected sensing data, we converted the analog dataset to short-term Fourier transform images. Subsequently, texture recognition was performed using a convolutional neural network with a classification accuracy of 97%.

Keywords

Acknowledgement

이 논문은 2022년도 정부(산업통상자원부)의 재원으로 산업기술평가관리원의 지원(No. RS-2022-00154781), 2020년도 정부(과학기술정보통신부)의 재원으로 정보통신기획평가원의 지원(No. 2020-0-00003), 2021년도 UST Young Scientist 양성 사업(No. 2021YS26)의 지원을 받아 수행된 연구임

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