Machine learning-driven metastructure design for sensor-free linearization of MEMS electrothermal actuators.

ABSTRACT: This study presents a novel approach for achieving linear motion in thermal micro-actuators by integrating machine learning-assisted optimized mechanical metastructures into the system design. Traditional solutions to actuator nonlinearity rely on complex sensor-based feedback mechanisms, which are often impractical in miniaturized systems. By embedding mechanical elements with tailored stiffness directly into the actuator structure, the proposed method transforms the inherent nonlinear relationship between input voltage and displacement into a near-linear response. A large design dataset was generated through finite element simulation and used to train a neural network model capable of predicting mechanical behavior across a broad design space. This model was then employed to guide invers

SUBMITTER: Zhang L 

PROVIDER: S-EPMC12612228 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature