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Wheel impact test by deep learning: prediction of location and magnitude of maximum stress

  • Industrial Application Paper
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Structural and Multidisciplinary Optimization Aims and scope Submit manuscript

Abstract

For ensuring vehicle safety, the impact performance of wheels during wheel development must be ensured through a wheel impact test. However, manufacturing and testing a real wheel requires a significant time and money because developing an optimal wheel design requires numerous iterative processes to modify the wheel design and verify the safety performance. Accordingly, wheel impact tests have been replaced by computer simulations such as finite element analysis (FEA); however, it still incurs high computational costs for modeling and analysis, and requires FEA experts. In this study, we present an aluminum road wheel impact performance prediction model based on deep learning that replaces computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass values used for the wheel impact test were utilized as the inputs to predict the magnitude of the maximum von Mises stress, corresponding location, and the stress distribution of the 2D disk-view. The input data were first compressed into a latent space with a 3D convolutional variational autoencoder (cVAE) and 2D convolutional autoencoder (cAE). Subsequently, the fully connected layers were used to predict the impact performance, and a decoder was used to predict the stress distribution heatmap of the 2D disk-view. The proposed model can replace the impact test in the early wheel-development stage by predicting the impact performance in real-time and can be used without domain knowledge. The time required for the wheel development process can be reduced using this mechanism.

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Acknowledgements

This work was supported by the Hyundai Motor Company, the National Research Foundation of Korea (2018R1A5A7025409), and the Ministry of Science and ICT of Korea (No. 2022-0-00969). Seungyeon Shin and Ah-hyeon Jin have contributed equally to this work.

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    Authors and Affiliations

    1. Cho Chun Shik Graduate School of Mobility, KAIST, Daejeon, 34051, South Korea

      Seungyeon Shin, Ah-hyeon Jin, Soyoung Yoo & Namwoo Kang

    2. Hyundai Motor Company, Hwaseong-Si, Gyeonggi-Do, 445706, South Korea

      ChangGon Kim & Sungpil Heo

    3. Narnia Labs, Daejeon, 34051, South Korea

      Soyoung Yoo, Sunghee Lee & Namwoo Kang

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    1. Seungyeon Shin
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    2. Ah-hyeon Jin
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    Correspondence to Namwoo Kang.

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    Shin, S., Jin, Ah., Yoo, S. et al. Wheel impact test by deep learning: prediction of location and magnitude of maximum stress. Struct Multidisc Optim 66, 24 (2023). https://doi.org/10.1007/s00158-022-03485-6

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    • DOI: https://doi.org/10.1007/s00158-022-03485-6

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