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Plateau stress estimation of aluminum foam by machine learning using X-ray computed tomography images

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Abstract

Aluminum foam is a multifunctional material with excellent shock absorption and heat-insulating properties and is expected to be used in many industrial fields. Since aluminum foam is fabricated by foaming aluminum, variations in pore structures can be observed even when foaming is conducted under the same conditions. Therefore, the establishment of a quality assurance method for the properties of aluminum foam as a product is a major issue. In this study, we attempted to estimate the plateau stress of aluminum foam by machine learning using only X-ray computed tomography (CT) images. A supervised learning neural network model was created using the obtained X-ray CT images of the entire three-dimensional structure and a set of plateau stresses obtained from actual compression tests of the corresponding aluminum foam. Using the created model, we showed that the plateau stress of a new aluminum foam compression specimen, which was different from the aluminum foam compression specimens used for training, can be estimated from X-ray CT images only. That is, it was shown that the mechanical properties of materials with complex geometries, such as foam, can be predicted nondestructively. In the future, it is expected to predict the mechanical properties of the foam by X-ray transmission and visual photographs, which are easier to obtain. We also showed that as the resolution of X-ray CT images improved, the mean error became smaller, namely, the estimation accuracy improved.

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The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.

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Acknowledgements

The authors would like to thank Osamu Kuwazuru of University of Fukui for his insightful comments and discussion.

Funding

This work was financially supported partly by Mitutoyo Association for Science and Technology (MAST) and JST-Mirai Program Grant Number JPMJMI19E5, Japan.

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Authors and Affiliations

  1. Faculty of Science and Technology, Gunma University, Kiryu, 376-8515, Japan

    Yoshihiko Hangai, Yuki Sakaguchi, Yuma Kitahara, Tatsuki Takagi, Okada Kenji & Tanaka Yuuki

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  1. Yoshihiko Hangai
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  2. Yuki Sakaguchi
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  3. Yuma Kitahara
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  4. Tatsuki Takagi
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Contributions

Yoshihiko Hangai: conceptualization, project administration, writing—original draft, writing—review and editing

Yuki Sakaguchi: data curation, formal analysis, investigation

Yuma Kitahara: data curation, investigation

Tatsuki Takagi: methodology, resources

Okada Kenji and Tanaka Yuuki: investigation, methodology, resources

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Correspondence to Yoshihiko Hangai.

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Hangai, Y., Sakaguchi, Y., Kitahara, Y. et al. Plateau stress estimation of aluminum foam by machine learning using X-ray computed tomography images. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13670-0

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