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Automatic strain sensor design via active learning and data augmentation for soft machines

Nature Machine Intelligence volume 4, pages 84–94 (2022)Cite this article

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An Author Correction to this article was published on 11 March 2022

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Abstract

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.

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Fig. 1: Three-stage framework for construction of a machine learning-enabled prediction model capable of automatic strain sensor design for soft machines.
Fig. 2: Boundary definition in sensor design space through an SVM.
Fig. 3: Progressive exploration of sensor design space through active learning.
Fig. 4: Model optimization through data augmentation and GA-based selection.
Fig. 5: Statistical analysis of data points collected from active learning.
Fig. 6: Automatic sensor design for soft machines.

Data availability

The data that support the plots within this paper and other findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

The Python code to implement the machine learning tasks within this study are available from GitHub (https://github.com/jiali1025/Automatic_Strain_Sensor_Design) or Zenodo (https://doi.org/10.5281/zenodo.5709148)57.

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Acknowledgements

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.).

Author information

Author notes

  1. These authors contributed equally: Haitao Yang, Jiali Li.

Authors and Affiliations

  1. Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore, Singapore

    Haitao Yang, Jiali Li, Kai Zhuo Lim, Chuanji Pan, Qian Wang, Kerui Li, Shuo Li, Meng Ding & Xiaoli Liu

  2. Engineering Product Development, Singapore University of Technology and Design, Singapore, Singapore

    Tien Van Truong & Pablo Valdivia y. Alvarado

  3. Department of Electrical and Electronic Engineering, Southern University of Science and Technology, Shenzhen, China

    Xiao Xiao

  4. Department of Chemical and Biomolecular Engineering, University of Maryland, College Park, MD, USA

    Tianle Chen & Po-Yen Chen

  5. School of Metallurgic Engineering, Anhui University of Technology, Ma’anshan, China

    Qian Xie

  6. Department of Chemical Engineering, Tsinghua University, Beijing, China

    Xiaonan Wang

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Contributions

P.-Y.C., X.W., J.L. and H.Y. conceived the project ideas and designed the experiments. H.Y. and S.L. carried out the synthesis of MXene nanosheets and characterizations. H.Y., K.Z.L. and J.L. designed the machine learning framework and implemented the machine learning tasks in Python. H.Y., Q.W., J.L. and C.P. collected the experimental data. T.V.T., Q.W. and H.Y. integrated the model-suggested sensors onto soft swimmer robot and performed related tests. X.X. and H.Y. performed the FEA simulations and analyses. P.-Y.C., X.W., J.L. and H.Y. interpreted the results and co-wrote the manuscript. K.L., S.L., M.D., T.C., X.L. and Q.X. involved in the discussion and manuscript revisions. P.-Y.C, P.V.A. and X.W. supervised this project.

Corresponding authors

Correspondence to Xiaonan Wang or Po-Yen Chen.

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Extended data

Extended Data Fig. 1 Influential fabrication parameters for tuning strain sensor characteristics.

(a) Schematic illustration of influential fabrication parameters for tuning strain sensor characteristics, including composition, thickness, and morphology of strain sensing layer. (b) Resistance–strain profiles of various G0 sensors with the sensing layers at different MXene/SWNT/PVA ratios. All G0 sensors were with the sensing layers at the same thickness of 800 nm. (c) Resistance–strain profiles of various G0 sensors with the sensing layers at different thicknesses. All G0 sensors were with the sensing layers at the same MXene/SWNT/PVA ratio of 90/5/5. (d) Resistance–strain profiles of Gn sensors with different sensing layer morphologies. All Gn sensors were composed of ps-MXene layers either with planar texture (G0), periodic wrinkles (G1–1D) or isotropic crumples (G1–2D). The MXene/SWNT/PVA ratio and thickness of all ps-MXene layers were controlled to be 100/0/0 and 800 nm, respectively.

Source data

Extended Data Fig. 2 In situ electron microscopic studies to validate data-driven design rules.

(a) SEM images of G0 ps-MXene layers with varying thicknesses under various uniaxial strains. Crack-to-width ratios (b) and crack densities (c) in G0 ps-MXene layers with varying thicknesses. All the ps-MXene layers kept the same MXene/SWNT/PVA ratio of 90/10/0. Crack-to-width ratios (d) and crack densities (e) in G0 ps-MXene layers with varying SWNT loadings (wt.%). All the ps-MXene layers kept the same thickness of 800 nm and 0 wt.% PVA loading. FEA simulation of strain distribution and in situ SEM images of G0 (f), G1–1D (g), and G1–2D (h) ps-MXene layers under various uniaxial strains.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–33, Figs. 1–32 and Tables 1–18.

Reporting Summary

Supplementary Video 1

A soft swimmer robot with model-suggested strain sensors for underwater exploration mission.

Supplementary Video 2

Real-time monitoring of a soft swimmer robot.

Source data

Source Data Fig. 1

Feasibility grades of detachment situations of 351 ps-MXene layers from PVDF membranes. Possibility heatmap representing the chances of successfully detaching ps-MXene layers from PVDF membranes.

Source Data Fig. 2

\({\bar L}\) and MSE values of trained navigation models by using different acquisition functions, including variance, random selection, distance and A Score. Cumulative number of fabricated Gn sensors for each loop of active learning; \({\bar{L}}\) and MSE values of evolving navigation model at different stages of active learning.

Source Data Fig. 3

Comparison of MRE values of various candidate models. Model-predicted strain labels and actual sensor characteristics of a G0 sensor. Model-predicted strain labels and actual sensor characteristics of a G1–1D sensor. Model-predicted strain labels and actual sensor characteristics of a G1–2D sensor.

Source Data Fig. 4

Classification of εmax extracted from 125 Gn sensors at different morphologies of strain-sensing layers. Spearman’s ρ statistical analyses for G1–2D, G1–1D and G0 sensors between fabrication parameters (for example, SWNT loading, PVA loading, and sensing layer thickness) and εmax. SHAP values of SWNT loading, PVA loading and sensing layer thickness for G1–2D, G1–1D and G0 sensors.

Source Data Fig. 5

GF-strain profiles of two suggested G1–1D sensors. Resistance–strain profile of Sensor 1 for monitoring a robotic task of grasping a candle. Resistance–strain profile of Sensor 1 for monitoring a robotic task of grasping a glue. GF-strain profiles of two suggested G0 sensors. Resistance–strain profile of an embedded G0 sensor (Sensor 2) in the left fin of a soft swimmer robot.

Source Data Extended Data Fig. 1

Resistance–strain profiles of various G0 sensors with the sensing layers at different MXene/SWNT/PVA ratios. Resistance–strain profiles of various G0 sensors with the sensing layers at different thicknesses. Resistance–strain profiles of Gn sensors with different sensing layer morphologies.

Source Data Extended Data Fig. 2

Crack-to-width ratios and crack densities in G0 ps-MXene layers with varying thicknesses and with varying SWNT loadings (wt%).

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Yang, H., Li, J., Lim, K.Z. et al. Automatic strain sensor design via active learning and data augmentation for soft machines. Nat Mach Intell 4, 84–94 (2022). https://doi.org/10.1038/s42256-021-00434-8

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