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Optimization of Optical Machine Structure by Backpropagation Neural Network Based on Particle Swarm Optimization and

Xinyong Zhang1,2,3, Liwei Sun1,2,3

  • 1Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China.

Materials (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

A new method accurately predicts optical machine structure mass and modal frequency. This approach optimizes lightweight design, demonstrating high efficiency and accuracy for optical machine structure optimization.

Keywords:
Bayesian regularization algorithmbackpropagation neural networkoptical machine structure optimizationparticle swarm optimizationspace camerasupporting structure

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Area of Science:

  • Mechanical Engineering
  • Computational Science
  • Optical Engineering

Background:

  • Accurate modeling of nonlinear relationships is crucial for optical machine structure optimization.
  • Existing methods face challenges in efficiently handling complex functional relationships between design variables and performance responses.

Purpose of the Study:

  • To propose and validate a novel prediction model for the mass and first-order modal frequency of supporting structures.
  • To utilize this model for optimizing the lightweight design of optical machine structures under modal frequency constraints.

Main Methods:

  • Development of a prediction model using a backpropagation neural network enhanced with particle swarm optimization and Bayesian regularization algorithms (BMPB).
  • Application of the BMPB model to predict mass and first-order modal frequency of a supporting structure.
  • Implementation of a mass-optimization process for the supporting structure with the first-order modal frequency as a constraint.

Main Results:

  • The developed prediction model achieved over 99% accuracy in predicting both mass and first-order modal frequency.
  • The optimization process demonstrated rapid convergence, highlighting the method's efficiency.
  • Validation on a supporting structure confirmed the high accuracy and efficiency of the proposed BMPB method.

Conclusions:

  • The BMPB method offers a highly accurate and efficient approach for predicting key parameters in optical machine structures.
  • This study provides an effective computational tool for the optimized lightweight design of optical machine structures.
  • The findings contribute to advancing the design process for complex optical systems requiring precise structural optimization.