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.

热驱动执行器的设计优化是一项艰巨的任务，因为其性能取决于多种材料参数，结构参数和工作条件。在这项工作中，我们采用了大规模有限元模拟以及机器学习算法，以实现热执行器的按需设计。使用有限元分析来模拟具有两层结构的热执行器的性能，该热执行器通过考虑参数的变化（包括模量，热膨胀系数，样品厚度和长度以及温度）来生成大量数据集。采用支持向量回归（SVR）来建立多个输入参数与所产生的接触压力之间的关系。之后，一个简单的内点算法被用来实现基于SVR模型的按需设计。由优化参数构成的热执行器的接触压力偏差小于目标值的15％。