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
- On-demand Direct Design of Polymeric Thermal Actuator by Machine Learning Algorithm
- Chinese Journal of Polymer Science 2020年38卷第8期页码：908-914
- 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：
  
  This work was financially supported by the National Natural Science Foundation of China (No. 51625303).
- DOI：10.1007/s10118-020-2396-8
  中图分类号：
- 收稿：2019-12-24，
  
  修回：2020-1-20，
  
  录用：2020-1-23，
  
  网络首发：2020-03-26，
  
  纸质出版：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

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