TY - JOUR
T1 - Automatic strain sensor design via active learning and data augmentation for soft machines
AU - Yang, Haitao
AU - Li, Jiali
AU - Lim, Kai Zhuo
AU - Pan, Chuanji
AU - Van Truong, Tien
AU - Wang, Qian
AU - Li, Kerui
AU - Li, Shuo
AU - Xiao, Xiao
AU - Ding, Meng
AU - Chen, Tianle
AU - Liu, Xiaoli
AU - Xie, Qian
AU - Alvarado, Pablo Valdivia y.
AU - Wang, Xiaonan
AU - Chen, Po Yen
N1 - Funding Information:
We thank C.-H. Yeow from the Department of Biomedical Engineering in the National University of Singapore for providing the soft gripper. We acknowledge the financial support provided by the Singapore RIE2020 Advanced Manufacturing and Engineering Programmatic Grant ‘Accelerated Materials Development for Manufacturing’ by the Agency for Science, Technology and Research under grant no. A1898b0043 (to X.W.). We acknowledge the financial support provided by the Start-Up Fund of University of Maryland, College Park (KFS no. 2957431 to P.-Y.C.). Funding for this research was provided by Maryland Industrial Partnerships under grant no. 6808 (KFS no. 4311103 to P.-Y.C.), Maryland Innovation Initiative (MII) Technology Assessment Award (KFS no. 4308302 to P.-Y.C.), and MOST-AFOSR Taiwan Topological and Nanostructured Materials Grant under grant no. FA2386-21-1-4065 (KFS no. 5284212 to P.-Y.C.).
Publisher Copyright:
© 2022, The Author(s), under exclusive licence to Springer Nature Limited.
PY - 2022/1
Y1 - 2022/1
N2 - Emerging soft machines require high-performance strain sensors to achieve closed-loop feedback control. Machine learning is a versatile tool to uncover complex correlations between fabrication recipes and sensor performance at the device level. Here a three-stage machine learning framework was realized for a high-accuracy prediction model capable of automating the design of strain sensors. First, a support-vector machine classifier was trained by using 351 compositions of various nanomaterials. Second, through 12 active learning loops, 125 strain sensors were stagewise fabricated to enrich the multidimensional dataset. Third, to address the challenge of data scarcity, data augmentation was implemented to synthesize >10,000 virtual data points, followed by genetic algorithm-based selection to optimize the model’s prediction accuracy. Several data-driven design rules for piezoresistive nanocomposites were generalized and validated by in situ microscopic studies. As final demonstrations, model-suggested strain sensors can be integrated into/onto various soft machines to endow them with real-time strain-sensing capabilities.
AB - Emerging soft machines require high-performance strain sensors to achieve closed-loop feedback control. Machine learning is a versatile tool to uncover complex correlations between fabrication recipes and sensor performance at the device level. Here a three-stage machine learning framework was realized for a high-accuracy prediction model capable of automating the design of strain sensors. First, a support-vector machine classifier was trained by using 351 compositions of various nanomaterials. Second, through 12 active learning loops, 125 strain sensors were stagewise fabricated to enrich the multidimensional dataset. Third, to address the challenge of data scarcity, data augmentation was implemented to synthesize >10,000 virtual data points, followed by genetic algorithm-based selection to optimize the model’s prediction accuracy. Several data-driven design rules for piezoresistive nanocomposites were generalized and validated by in situ microscopic studies. As final demonstrations, model-suggested strain sensors can be integrated into/onto various soft machines to endow them with real-time strain-sensing capabilities.
UR - http://www.scopus.com/inward/record.url?scp=85123635789&partnerID=8YFLogxK
U2 - 10.1038/s42256-021-00434-8
DO - 10.1038/s42256-021-00434-8
M3 - Article
AN - SCOPUS:85123635789
SN - 2522-5839
VL - 4
SP - 84
EP - 94
JO - Nature Machine Intelligence
JF - Nature Machine Intelligence
IS - 1
ER -