Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning

Jimin Gu; Yongsu Jung; Junseong Ahn; Jihyeon Ahn; Jungrak Choi; Byeongmin Kang; Yongrok Jeong; Ji Hwan Ha; Taehwan Kim; Young Jung; Jaeho Park; Jiyoung Jung; Seunghwa Ryu; Ikjin Lee; Inkyu Park

doi:10.1016/j.nanoen.2024.110124

Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning

Jimin Gu, Yongsu Jung, Junseong Ahn, Jihyeon Ahn, Jungrak Choi, Byeongmin Kang, Yongrok Jeong, Ji Hwan Ha, Taehwan Kim, Young Jung, Jaeho Park, Jiyoung Jung, Seunghwa Ryu, Ikjin Lee, Inkyu Park

School of Mechanical Engineering

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

Recently, soft material based wearable sensors have discovered numerous applications in healthcare, sports monitoring, and virtual reality/augmented reality (VR/AR) systems. For these sensors, fulfilling user-specified requirements rather than just improving the sensor performance has become an important issue. In this study, a self-powered piezo-transmittance type strain sensor based on auxetic structures was optimized for configurable and user-specified characteristics using a machine-learning surrogate model. The sensor mechanism is based on the optical transmittance change induced by the gap opening of the auxetic kirigami structure. The sensor performance was analyzed according to the geometric design variables, and the optimal design was determined using Bayesian and Gaussian process to maximize the sensor performance for different purposes. The optimally designed geometries were used for self-powered sensors on a structural health monitoring (SHM) system, a human motion monitoring (HMM) system for monitoring sports performance and incorporated into an AR system.

Original language	English
Article number	110124
Journal	Nano Energy
Volume	130
DOIs	https://doi.org/10.1016/j.nanoen.2024.110124
State	Published - Nov 2024

Keywords

AR wearable system
Auxetic structure
Customizable performance
Machine learning
Self-powered sensor
Strain sensor

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.nanoen.2024.110124

Cite this

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title = "Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning",

abstract = "Recently, soft material based wearable sensors have discovered numerous applications in healthcare, sports monitoring, and virtual reality/augmented reality (VR/AR) systems. For these sensors, fulfilling user-specified requirements rather than just improving the sensor performance has become an important issue. In this study, a self-powered piezo-transmittance type strain sensor based on auxetic structures was optimized for configurable and user-specified characteristics using a machine-learning surrogate model. The sensor mechanism is based on the optical transmittance change induced by the gap opening of the auxetic kirigami structure. The sensor performance was analyzed according to the geometric design variables, and the optimal design was determined using Bayesian and Gaussian process to maximize the sensor performance for different purposes. The optimally designed geometries were used for self-powered sensors on a structural health monitoring (SHM) system, a human motion monitoring (HMM) system for monitoring sports performance and incorporated into an AR system.",

keywords = "AR wearable system, Auxetic structure, Customizable performance, Machine learning, Self-powered sensor, Strain sensor",

author = "Jimin Gu and Yongsu Jung and Junseong Ahn and Jihyeon Ahn and Jungrak Choi and Byeongmin Kang and Yongrok Jeong and Ha, {Ji Hwan} and Taehwan Kim and Young Jung and Jaeho Park and Jiyoung Jung and Seunghwa Ryu and Ikjin Lee and Inkyu Park",

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doi = "10.1016/j.nanoen.2024.110124",

language = "English",

volume = "130",

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AU - Ahn, Junseong

AU - Ahn, Jihyeon

AU - Choi, Jungrak

AU - Kang, Byeongmin

AU - Jeong, Yongrok

AU - Ha, Ji Hwan

AU - Kim, Taehwan

AU - Jung, Young

AU - Park, Jaeho

AU - Jung, Jiyoung

AU - Ryu, Seunghwa

AU - Lee, Ikjin

AU - Park, Inkyu

PY - 2024/11

Y1 - 2024/11

N2 - Recently, soft material based wearable sensors have discovered numerous applications in healthcare, sports monitoring, and virtual reality/augmented reality (VR/AR) systems. For these sensors, fulfilling user-specified requirements rather than just improving the sensor performance has become an important issue. In this study, a self-powered piezo-transmittance type strain sensor based on auxetic structures was optimized for configurable and user-specified characteristics using a machine-learning surrogate model. The sensor mechanism is based on the optical transmittance change induced by the gap opening of the auxetic kirigami structure. The sensor performance was analyzed according to the geometric design variables, and the optimal design was determined using Bayesian and Gaussian process to maximize the sensor performance for different purposes. The optimally designed geometries were used for self-powered sensors on a structural health monitoring (SHM) system, a human motion monitoring (HMM) system for monitoring sports performance and incorporated into an AR system.

AB - Recently, soft material based wearable sensors have discovered numerous applications in healthcare, sports monitoring, and virtual reality/augmented reality (VR/AR) systems. For these sensors, fulfilling user-specified requirements rather than just improving the sensor performance has become an important issue. In this study, a self-powered piezo-transmittance type strain sensor based on auxetic structures was optimized for configurable and user-specified characteristics using a machine-learning surrogate model. The sensor mechanism is based on the optical transmittance change induced by the gap opening of the auxetic kirigami structure. The sensor performance was analyzed according to the geometric design variables, and the optimal design was determined using Bayesian and Gaussian process to maximize the sensor performance for different purposes. The optimally designed geometries were used for self-powered sensors on a structural health monitoring (SHM) system, a human motion monitoring (HMM) system for monitoring sports performance and incorporated into an AR system.

KW - AR wearable system

KW - Auxetic structure

KW - Customizable performance

KW - Machine learning

KW - Self-powered sensor

KW - Strain sensor

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Auxetic kirigami structure-based self-powered strain sensor with customizable performance using machine learning

Abstract

Keywords

UN SDGs

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