Abstract
The design optimization of thermal-driven actuators is a challenging task because the performance depends on multiple materials parameters, structural parameters, and working conditions. In this work, we adopted large scale finite element simulation together with machine learning algorithm to fulfill the on-demand design of thermal actuators. Finite element analysis was used to simulate the performance of thermal actuator with two-layer structure, which generated large amount of dataset by considering the variation of parameters including the moduli, thermal expansion coefficient, sample thickness and length, and temperature. Support vector regression (SVR) was adopted to establish the relationship between multiple input parameters and the resulting contact pressure. Thereafter, a simple interior point algorithm was used to achieve the on-demand design based on the SVR model. The contact pressures of thermal actuator constructed from the optimized parameters deviated less than 15% of the target values.
Similar content being viewed by others
References
Jiang, W.; Niu, D.; Liu, H.; Wang, C.; Zhao, T.; Yin, L.; Shi, Y.; Chen, B.; Ding, Y.; Lu, B. Photoresponsive soft-robotic platform: Biomimetic fabrication and remote actuation. Adv. Funct. Mater.2014, 24, 7598–7604.
Zheng, W. J.; An, N.; Yang, J. H.; Zhou, J.; Chen, Y. M. Tough Alalginate/poly(N-isopropylacrylamide) hydrogel with tunable LCST for soft robotics. ACS Appl. Mater. Interfaces2015, 7, 1758–1764.
Xu, B.; Jiang, H.; Li, H.; Zhang, G.; Zhang, Q. High strength nanocomposite hydrogel bilayer with bidirectional bending and shape switching behaviors for soft actuators. RSC Adv.2015, 5, 13167–13170.
Haines, C. S.; Lima, M. D.; Li, N.; Spinks, G. M.; Foroughi, J.; Madden, J. D. W.; Kim, S. H.; Fang, S.; de Andrade, M. J.; Göktepe, F., Göktepe, Ö.; Mirvakili, S. M.; Naficy, S.; Lepró, X.; Oh, J.; Kozlov, M. E.; Kim, S. J.; Xu, X.; Swedlove, B. J.; Wallace, G. G.; Baughman, R. H. Artificial muscles from fishing line and sewing thread. Science2014, 343, 868–872.
Liu, T. Y.; Hu, S. H.; Liu, T. Y.; Liu, D. M.; Chen, S. Y. Magnetic-sensitive behavior of intelligent ferrogels for controlled release of drug. Langmuir2006, 22, 5974–5978.
Wang, H.; Wang, Y.; Tee, B. C.; Kim, K.; Lopez, J.; Cai, W.; Bao, Z. Shape-controlled, self-wrapped carbon nanotube 3D electronics. Adv. Sci.2015, 2, 1500103.
Hu, Y.; Chen, W. Externally induced thermal actuation of polymer nanocomposites. Macromol. Chem. Phys.2011, 212, 992–998.
Kumar, K.; Knie, C.; Bleger, D.; Peletier, M. A.; Friedrich, H.; Hecht, S.; Broer, D. J.; Debije, M. G.; Schenning, A. P. A chaotic selfoscillating sunlight-driven polymer actuator. Nat. Commun.2016, 7, 11975.
Chen, T.; Bakhshi, H.; Liu, L.; Ji, J.; Agarwal, S. Combining 3D printing with electrospinning for rapid response and enhanced designability of hydrogel actuators. Adv. Funct. Mater.2018, 28, 1800514.
Chen, L.; Weng, M.; Zhang, W.; Zhou, Z.; Zhou, Y.; Xia, D.; Li, J.; Huang, Z.; Liu, C.; Fan, S. Transparent actuators and robots based on single-layer superaligned carbon nanotube sheet and polymer composites. Nanoscale2016, 8, 6877–83.
Wang, C.; Wang, Y.; Yao, Y.; Luo, W.; Wan, J.; Dai, J.; Hitz, E.; Fu, K. K.; Hu, L. A solution-processed high-temperature, flexible, thinfilm actuator. Adv. Mater.2016, 28, 8618–8624.
Deng, J.; Li, J.; Chen, P.; Fang, X.; Sun, X.; Jiang, Y.; Weng, W.; Wang, B.; Peng, H. Tunable photothermal actuators based on a pre-programmed aligned nanostructure. J. Am. Chem. Soc.2016, 138, 225–230.
Luo, Z.; Tong, L.; Ma, H. Shape and topology optimization for electrothermomechanical microactuators using level set methods. J. Comput. Phys.2009, 228, 3173–3181.
Sourmail, T.; Bhadeshia, H. K. D. H.; MacKay, D. J. C. Neural network model of creep strength of austenitic stainless steels. Mater. Sci. Technol.2002, 18, 655–663.
Suh, C.; Rajan, K. Data mining and informatics for crystal chemistry: establishing measurement techniques for mapping structure-property relationships. Mater. Sci. Technol.2009, 25, 466–471.
Tang, J. L.; Cai, Q. R.; Liu, Y. J. In Prediction of material mechanical properties with support vector machine. 2010 International Conference on Machine Vision and Human-Machine Interface, MVHI 2010, 2010, pp 592−595.
Hornik, K.; Stinchcombe, M.; White, H. Multilayer feedforward networks are universal approximators. Neural Networks1989, 2, 359–366.
Wang, J.; Chen, Q.; Chen, Y. RBF kernel based support vector machine with universal approximation and its application. Lec. Notes. Comput. Sci.2004, 3173, 512–517.
Nagatani, H.; Imou, A. Contact pressure and shear stress analysis on conforming contact problem. J. Adv. Mech. Des. Syst2008, 2, 1055–1066.
Alex, J.; Smola, B. S. L. A tutorial on support vector regression. Stat. Comput.2004, 14, 199–222.
Shergold, O. A.; Fleck, N. A.; Radford, D. The uniaxial stress versus strain response of pig skin and silicone rubber at low and high strain rates. Int. J. Impact Eng.2006, 32, 1384–1402.
Blokland, R.; Prins, W. Elasticity and structure of chemically crosslinked polyurethanes. J. Polym. Sci., Part A: Polym. Chem.1969, 7, 1595–1618.
Fan, H.; Wang, J.; Jin, Z. Tough, swelling-resistant, self-healing, and adhesive dual-cross-linked hydrogels based on polymer–tannic acid multiple hydrogen bonds. Macromolecules2018, 51, 1696–1705.
Dizqah, A. M.; Maheri, A.; Busawon, K. An accurate method for the PV model identification based on a genetic algorithm and the interior-point method. Renew. Energy2014, 72, 212–222.
Acknowledgments
This work was financially supported by the National Natural Science Foundation of China (No. 51625303).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Liu, BE., Yu, W. On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm. Chin J Polym Sci 38, 908–914 (2020). https://doi.org/10.1007/s10118-020-2396-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10118-020-2396-8