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  3. A Hybrid Preprocessing Multi-objective Surrogate Model For Thermal Mems Actuators.
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  3. A Hybrid Preprocessing Multi-objective Surrogate Model For Thermal Mems Actuators.

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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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A Hybrid Preprocessing Multi-Objective Surrogate Model for Thermal MEMS Actuators.

Armin Aghajani1, Ali Nazari1, Phiona Buhr1

  • 1Department of Electrical and Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada.

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a surrogate model for predicting MEMS actuator performance. Gaussian Process Regression (GPR) offers higher accuracy, while ensemble models train faster, aiding design optimization.

Keywords:
Gaussian Process Regression (GPR)MEMS thermal actuatorsensemble learningfinite element method (FEM)multi-objective optimizationsurrogate modeling

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Area of Science:

  • Mechanical Engineering
  • Computational Science
  • Materials Science

Background:

  • Micro-Electro-Mechanical Systems (MEMS) actuators require complex simulations for design.
  • Predicting multiple performance metrics simultaneously is computationally intensive.
  • Efficient surrogate modeling can accelerate the MEMS design cycle.

Purpose of the Study:

  • To develop and compare advanced surrogate models for multi-objective prediction of MEMS actuator performance.
  • To evaluate the accuracy and efficiency of Gaussian Process Regression (GPR) and an ensemble model (Random Forest and XGBoost).
  • To assess the impact of sampling and preprocessing strategies on model scalability and computational cost.

Main Methods:

  • Generation of 10,000 design samples using Latin Hypercube sampling.
  • COMSOL Multiphysics simulations for data generation.
  • Implementation and comparison of Gaussian Process Regression (GPR) and an ensemble model (Random Forest, XGBoost).
  • Application of eight preprocessing techniques and 5-fold cross-validation.

    • Main Results:

    • GPR achieved a Mean Absolute Percentage Error (MAPE) between 0.81% and 2.58% for five key output variables.
    • The ensemble model exhibited a MAPE ranging from 3.05% to 9.20%.
    • GPR demonstrated superior prediction accuracy, while the ensemble model provided faster training times.

    Conclusions:

    • The proposed surrogate model effectively predicts multiple MEMS actuator performance metrics.
    • GPR offers higher accuracy, making it suitable for precise design predictions.
    • The study demonstrates a significant reduction in computational costs and acceleration of the MEMS actuator design process.