Application of modal analysis to multi-objective optimization of gear box

The high stiffness and low mass gearbox structure design requirements were met through multi-objective optimization based on finite element modal analysis. The correlation between structural parameters and optimization objectives was identified, providing guidance for optimal design. A parametric finite element model of the box cover was established to calculate the first-order modal shape and equivalent stiffness. By using mass and equivalent stiffness as optimization objectives, a discrete data set was constructed with key dimensions of the box cover as design variables, from which a surrogate model was obtained. Standard response surface and full second-order polynomial fitted response surface function were applied after error verification. Different optimization algorithms were employed to search for the optimal value, resulting in obtaining design variables meeting the optimization objectives. The results indicated a reduction in optimized mass by approximately 6.8 %, with an error of less than 1 %.

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Publication Details

Journal
Vibroengineering PROCEDIA
Published
2024-09-27
DOI
https://doi.org/10.21595/vp.2024.24162
Primary Topic
Mechanical Engineering Research and Applications
Type
article
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article

Application of modal analysis to multi-objective optimization of gear box

Sen Zhang
Vibroengineering PROCEDIA
Mechanical Engineering Research and Applications
article

Application of modal analysis to multi-objective optimization of gear box

Sen Zhang
article en

Abstract

The high stiffness and low mass gearbox structure design requirements were met through multi-objective optimization based on finite element modal analysis. The correlation between structural parameters and optimization objectives was identified, providing guidance for optimal design. A parametric finite element model of the box cover was established to calculate the first-order modal shape and equivalent stiffness. By using mass and equivalent stiffness as optimization objectives, a discrete data set was constructed with key dimensions of the box cover as design variables, from which a surrogate model was obtained. Standard response surface and full second-order polynomial fitted response surface function were applied after error verification. Different optimization algorithms were employed to search for the optimal value, resulting in obtaining design variables meeting the optimization objectives. The results indicated a reduction in optimized mass by approximately 6.8 %, with an error of less than 1 %.

Vibroengineering PROCEDIAVol. 55
Openalex Percentile: Top 84%
Mechanical Engineering Research and Applications
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