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Lifetime Prediction of Silicone and Direct Ink Writing-Based Soft Sensors Under Cyclic Strain

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Abstract

The softness and stretchability of soft sensors has generated much interest with respect to applying soft sensors for human activity monitoring and proprioception of soft robots. However, most of the research in this area has focused on electrical stability, despite the importance of mechanical failure, thus limiting practical application. In this study, the lifetime of silicone-based soft sensors was examined under accelerated cyclic strain conditions, to construct lifetime prediction models of crack nucleation and growth considering the failure properties of the sensor’s silicone elastomer. To establish the models, an accelerated life test was conducted, in which the lifetime was estimated according to a Weibull distribution under accelerated cyclic strain conditions. Specifically, a lifetime prediction model using the crack growth approach (CGA) was constructed by experimentally measuring the energy release rate (tearing energy) of the silicone elastomer due to crack propagation. Compared to the inverse power law-based model, the CGA-based model showed about 90% improvement in lifetime prediction accuracy in the strain ranges from 150 to 270% with root mean square error 456 and 4592 cycles, respectively, thus indicating that tearing energy is an important parameter for sensor lifetime prediction. The proposed model is expected to be useful for predicting the lifetime of soft sensors under various strain operating conditions.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIT) (No. NRF-2019R1A2C2084677 and NRF-2016R1A5A1938472), and Korea Evaluation Institute of Industrial Technology (KEIT) grant funded by the Korea Government (No. 20008912).

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  1. Department of Mechanical Engineering, Ulsan Institute of Science and Technology (UNIST), Ulsan, Korea

    Kyeongtaek Kim & Joonbum Bae

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  1. Kyeongtaek Kim
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Kim, K., Bae, J. Lifetime Prediction of Silicone and Direct Ink Writing-Based Soft Sensors Under Cyclic Strain. Int. J. of Precis. Eng. and Manuf.-Green Tech. 10, 535–546 (2023). https://doi.org/10.1007/s40684-022-00469-1

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