On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm

Bo-En Liu; Wei Yu

doi:10.1007/s10118-020-2396-8

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On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm

ARTICLE | Updated：2020-03-26

- On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm
- Chinese Journal of Polymer Science Vol. 38, Issue 8, Pages: 908-914(2020)
- Affiliations：
  
  Advanced Rheology Institute, Department of Polymer Science and Engineering, State Key Laboratory for Metal Matrix Composite Materials, Shanghai Key Laboratory of Electrical Insulation and Thermal Ageing, Shanghai Jiao Tong University, Shanghai 200240, China
- Author bio：
  
  wyu@sjtu.edu.cn
- Funds：
- DOI：10.1007/s10118-020-2396-8
  CLC：
- Received：24 December 2019，
  
  Revised：2020-1-20，
  
  Accepted：23 January 2020，
  
  Online First：26 March 2020，
  
  Published：2020-08
- Accepted：
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Liu, B. E.; Yu, W. On-demand direct design of polymeric thermal actuator by machine learning algorithm. Chinese J. Polym. Sci. 2020, 38, 908–914

Bo-En Liu, Wei Yu. On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm[J]. Chinese Journal of Polymer Science, 2020, 38(8): 908-914.
Liu, B. E.; Yu, W. On-demand direct design of polymeric thermal actuator by machine learning algorithm. Chinese J. Polym. Sci. 2020, 38, 908–914 DOI： 10.1007/s10118-020-2396-8.

Bo-En Liu, Wei Yu. On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm[J]. Chinese Journal of Polymer Science, 2020, 38(8): 908-914. DOI： 10.1007/s10118-020-2396-8.

摘要

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.

关键词

Keywords

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 . DOI: 10.1002/adfm.201402070 http://doi.org/10.1002/adfm.201402070 .

Zheng, W. J.; An, N.; Yang, J. H.; Zhou, J.; Chen, Y. M. . Tough Al-alginate/poly( N -isopropylacrylamide) hydrogel with tunable LCST for soft robotics . ACS Appl. Mater. Interfaces , 2015 . 7 1758 - 1764 . DOI: 10.1021/am507339r http://doi.org/10.1021/am507339r .

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 . DOI: 10.1039/C4RA14545C http://doi.org/10.1039/C4RA14545C .

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 . Science , 2014 . 343 868 - 872 . DOI: 10.1126/science.1246906 http://doi.org/10.1126/science.1246906 .

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 . Langmuir , 2006 . 22 5974 - 5978 . DOI: 10.1021/la060371e http://doi.org/10.1021/la060371e .

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 DOI: 10.1002/advs.201500103 http://doi.org/10.1002/advs.201500103 .

Hu, Y.; Chen, W. . Externally induced thermal actuation of polymer nanocomposites . Macromol. Chem. Phys. , 2011 . 212 992 - 998 . DOI: 10.1002/macp.201100068 http://doi.org/10.1002/macp.201100068 .

Kumar, K.; Knie, C.; Bleger, D.; Peletier, M. A.; Friedrich, H.; Hecht, S.; Broer, D. J.; Debije, M. G.; Schenning, A. P. . A chaotic self-oscillating sunlight-driven polymer actuator . Nat. Commun. , 2016 . 7 11975 DOI: 10.1038/ncomms11975 http://doi.org/10.1038/ncomms11975 .

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 DOI: 10.1002/adfm.201800514 http://doi.org/10.1002/adfm.201800514 .

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 . Nanoscale , 2016 . 8 6877 - 83 . DOI: 10.1039/C5NR07237A http://doi.org/10.1039/C5NR07237A .

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, thin-film actuator . Adv. Mater. , 2016 . 28 8618 - 8624 . DOI: 10.1002/adma.201602777 http://doi.org/10.1002/adma.201602777 .

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 . DOI: 10.1021/jacs.5b10131 http://doi.org/10.1021/jacs.5b10131 .

Luo, Z.; Tong, L.; Ma, H. . Shape and topology optimization for electrothermomechanical microactuators using level set methods . J. Comput. Phys. , 2009 . 228 3173 - 3181 . DOI: 10.1016/j.jcp.2009.01.010 http://doi.org/10.1016/j.jcp.2009.01.010 .

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 . DOI: 10.1179/026708302225002065 http://doi.org/10.1179/026708302225002065 .

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 . DOI: 10.1179/174328409X430483 http://doi.org/10.1179/174328409X430483 .

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 Networks , 1989 . 2 359 - 366 . DOI: 10.1016/0893-6080(89)90020-8 http://doi.org/10.1016/0893-6080(89)90020-8 .

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. Syst , 2008 . 2 1055 - 1066 . DOI: 10.1299/jamdsm.2.1055 http://doi.org/10.1299/jamdsm.2.1055 .

Alex, J.; Smola, B. S. L. . A tutorial on support vector regression . Stat. Comput. , 2004 . 14 199 - 222 . DOI: 10.1023/B:STCO.0000035301.49549.88 http://doi.org/10.1023/B:STCO.0000035301.49549.88 .

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 . DOI: 10.1016/j.ijimpeng.2004.11.010 http://doi.org/10.1016/j.ijimpeng.2004.11.010 .

Blokland, R.; Prins, W. . Elasticity and structure of chemically crosslinked polyurethanes . J. Polym. Sci., Part A: Polym. Chem. , 1969 . 7 1595 - 1618 . DOI: 10.1002/pol.1969.160070913 http://doi.org/10.1002/pol.1969.160070913 .

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 . Macromolecules , 2018 . 51 1696 - 1705 . DOI: 10.1021/acs.macromol.7b02653 http://doi.org/10.1021/acs.macromol.7b02653 .

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. Energy , 2014 . 72 212 - 222 . DOI: 10.1016/j.renene.2014.07.014 http://doi.org/10.1016/j.renene.2014.07.014 .

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