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説明:

Deformation of an aluminum alloy sheet which is affected by its underlying crystallographic texture has been widely studied using crystal plasticity finite element method (CPFEM). Numerical material test based on the CPFEM allows us to quantitatively estimate stress-strain curve and the Lankford value (r-value) of aluminum alloy sheets that depend on the texture. However, to utilize the numerical material test as a means of optimizing the texture to design aluminum alloys, the CPFEM is computationally expensive. In this study, we propose a methodology for rapidly estimating the stress-strain curve and r-value of aluminum alloy sheets using the deep learning with a neural network. We train the neural network with synthetic texture and stress-strain curves calculated by the numerical material test. To capture the feature of synthetic texture from an {111} pole figure image, the neural network incorporates the convolution neural network. Using the trained neural network, we can estimate the uniaxial stress-strain curve and the in-plane anisotropy of r-value for various textures which contain Cube and S components. The results demonstrate that the neural network
trained with the results of the numerical material test is a promising methodology for rapidly estimating the deformation of aluminum alloy sheets.

権利情報:

• In Copyright
  In Copyright

キーワード: texture, tension test, numerical analysis, crystallite orientation, flow stress, deep learning

刊行年月日: 2020-12-01

出版者: Japan Institute of Metals

掲載誌:

• MATERIALS TRANSACTIONS (ISSN: 13459678) vol. 61 issue. 12 p. 2276-2283

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.2320/matertrans.P-M2020853

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更新時刻: 2024-01-05 22:12:43 +0900

MDRでの公開時刻: 2023-03-02 13:22:12 +0900