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Machine learning accelerated MMC-based topology optimization for sound quality enhancement of serialized acoustic structures

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Abstract

A key requirement for product design is the ability to capture the physical features of structures quickly and efficiently. One way to achieve this purpose is to use machine learning to support topology optimization. However, due to the diversity of application requirements in product updates, the machine learning-based optimization model needs to be frequently rebuilt by collecting large amounts of data. In this work, Moving Morphable Component approach is combined with Deep Neural Network to achieve the quick topology optimization design of acoustic devices. A deep transfer learning approach to the predictive model is also developed for the model to be adapted to different design conditions. Thus, using the proposed approach, not only the originally designed acoustic structure can be quickly optimized, but also the serialized structures evolved from the source model can be easily obtained. Numerical examples illustrate the effectiveness and advantages of the proposed method.

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Acknowledgements

The financial support from the Foundation for Innovative Research Groups of the National Natural Science Foundation, China (11821202), the National Natural Science Foundation, China (12272075), Liao Ning Revitalization Talents Program (XLYC2001003, XLYC1907119), Fundamental Research Funds for the Central Universities, China (DUT22QN238), Program for Changjiang Scholars, Innovative Research Team in University (PCSIRT), and 111 Project, China (B14013) are gratefully acknowledged.

Funding

This work was funded by the Foundation for Innovative Research Groups of the National Natural Science Foundation (Grant No. 11821202).

Author information

Authors and Affiliations

  1. Department of Engineering Mechanics, State Key Laboratory of Structural Analysis for Industrial Equipment, International Research Center for Computational Mechanics, Dalian University of Technology, Dalian, 116023, China

    Lei Xu, Weisheng Zhang, Sung-Kie Youn & Xu Guo

  2. National Innovation Institute of Defense Technology, Chinese Academy of Military Science, Beijing, 100000, China

    Wen Yao

  3. Department of Mechanical Engineering, KAIST, 291 Daehakro, Daejeon, 34141, Republic of Korea

    Sung-Kie Youn

Authors
  1. Lei Xu
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  2. Weisheng Zhang
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  3. Wen Yao
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  4. Sung-Kie Youn
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  5. Xu Guo
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Contributions

Weisheng Zhang (weishengzhang@dlut.edu.cn) and Xu Guo (guoxu@dlut.edu.cn) are co-corresponding authors of this paper.

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Correspondence to Weisheng Zhang.

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Upon request, the authors will provide the full set of input parameters for each validation problems presented in the paper.

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Xu, L., Zhang, W., Yao, W. et al. Machine learning accelerated MMC-based topology optimization for sound quality enhancement of serialized acoustic structures. Struct Multidisc Optim 67, 85 (2024). https://doi.org/10.1007/s00158-024-03800-3

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  • DOI: https://doi.org/10.1007/s00158-024-03800-3

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